Edge device appliance network and systems and methods for energy consumption control and savings

Abstract

A method is provided for reducing energy consumption and / or demand of an edge device appliance involves receiving conditioning data pertaining to a thermostatic or other control output of the edge device appliance for a conditioned space or other load, processing the conditioning data using artificial intelligence (Al) software to create an optimized thermostatic or other control signal for the edge device appliance in the conditioned space or other load, and controlling the edge device appliance for the conditioned space or other load based on the optimized thermostatic or other control signal. The method can further include receiving an energy consumption and / or demand modification command. Power consumption data can be received to determine real-time energy consumption and demand data of the edge device appliance. An energy consumption and / or demand modification signal can be generated using the Al software and used to control the edge device appliance.

Description

EDGE DEVICE APPLIANCE NETWORK AND SYSTEMS AND METHODS FOR ENERGY CONSUMPTION CONTROL AND SAVINGSCROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit under 35 U.S.C. §119(e) of prior U.S. Provisional Patent Application No. 63 / 616,863, filed January 2, 2024, which is incorporated herein in its entirety by reference.BACKGROUND OF THE INVENTION

[0002] Electrical power grids around the world are currently reaching capacity due to load growth, particularly from building and transport electrification and from data center operations. Additionally, daily electrical load peak usage often results in power grid operators having to bring on more electrical generation with higher carbon dioxide equivalent (CO2e) emissions. More intelligent, bilateral, and real-time approaches to match electricity supply and demand are needed. Three general approaches are currently in use and in development, which include: (1) permanent demand reduction by means of improved, more energy-efficient electrical equipment designs and by retrofitting of existing equipment, (2) electric load shedding, also called demand response, which includes commanded reduction in load from a signal generated by a power grid operator, and (3) load shifting, from higher-demand and high-cost periods to lower-demand and lower-cost periods. Additionally, natural gas usage has increased worldwide as a fraction of total energy consumption for electric power generation, space heating, and industrial purposes. In natural gas distribution networks, there are thus increasing needs for permanent demand reduction and load shedding and shifting, similar to the approaches of (1)- (3) above.

[0003] Heating, ventilating, air conditioning and refrigeration (also known as HVACR and HVAC&R) cooling and heating loads are candidates for both demand response and load shifting. In the case of demand response, the HVACR unit may not be operating at full design capacity during the demand response event and in the case of load shifting, it is possible to utilize weather and also electric price signal information to shift HVACR electric demand to lower-demand or lower-cost times of the day. Mechanisms that accomplish HVACR load shifting include pre-cooling (e.g., cooling a building or other load overnight, before a hot day) and pre-heating (e.g., heating a building overnight before the morning peak demand).

[0004] In pursuit of both HVACR or other load demand response and load shifting, various thermostatically -based systems have been proposed. For electrical demand response, most of the thermostatically-based systems manipulate the thermostatic setpoint, and consequentlyresult in potential discomfort to the occupants of the building. Additionally, current technology used for load shifting, such as pre-heating and pre-cooling a building, also generally relies upon setpoint changes that may also affect occupant comfort.

[0005] Given the enormous CO2e impact of leaked air conditioning and refrigeration refrigerants and the rapid increase in air conditioning installations around the world as projected over the next several decades, refrigerant monitoring and management is a critical element in climate change mitigation that is currently very poorly managed at the unitary HVACR level, from the residential level up through large fleets of commercial and industrial HVACR units.

[0006] It is thus desirable to provide original and / or retrofittable smart technology that provides demand reduction, electric load shedding, and / or load shifting for appliances of HVACR systems, without the comfort penalties associated with other current approaches, and with additional beneficial features as described herein.SUMMARY OF THE PRESENT INVENTION

[0007] A feature of the present invention is to provide a method to automatically manage and provide energy and demand control and energy savings and demand reduction estimation for operating (duty cycled) HVACR and other load equipment in an improved manner as compared to operation with the original load controls. As an example of other load equipment, a compressed air bank can be managed.

[0008] A further feature of the present invention is to provide an embodiment as an electronic controller that can be used as an add-on, for example, a retrofit, device in HVACR and other load systems, which automatically manages and provides energy and demand control and energy savings and demand reduction estimation of operating HVACR or other equipment, based on Al-based commands.

[0009] Another feature of the present invention is to provide systems which incorporate the indicated controller to automatically manage and provide energy and demand control and savings estimation of operating HVACR equipment in the system.

[0010] Additional features and advantages of the present invention will be set forth in part in the description that follows, and in part will be apparent from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention will be realized and attained by means of the elements and combinations particularly pointed out in the description and appended claims.

[0011] To achieve these and other advantages, and in accordance with the purposes of the present invention, as embodied and broadly described herein, the present invention relates to a method for automatically controlling and managing energy consumption, demand, and operation of at least one load unit powered by electricity in an HVACR or other system to obtain a selected level of energy savings and demand reduction, with other benefits as described herein. The method can comprise the step of receiving conditioning data pertaining to a thermostatic output of an edge device appliance in a conditioned space, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs. The method can comprise processing the conditioning data using artificial intelligence (Al) software to create an optimized thermostatic control signal for the edge device appliance in the conditioned space. The method can comprise controlling the edge device appliance in the conditioned space based on the optimized thermostatic control signal. The method can comprise receiving an energy consumption or demand modification command, the energy consumption or demand modification command including or activating a retrieval of modification data, the modification data comprising a demand reduction command value, a time-of-day energy rate, weather information, or a combination thereof. The method can comprise receiving power consumption data from sensors to determine real-time energy consumption and demand data of the edge device appliance in the conditioned space. The method can comprise generating an energy consumption and / or demand reduction modification signal using the Al software, the energy consumption and / or demand reduction modification signal being based on the conditioning data, the modification data, and the real-time energy consumption and demand data. The method can comprise controlling the edge device appliance in the conditioned space based on the energy consumption modification signal, wherein the energy consumption and / or demand modification signal comprises a modification of timing of component commands for the HVACR or other system to achieve energy consumption and / or demand reduction.

[0012] Original, thermostatic, and edge node devices, networks, and systems for carrying out the methods are also provided.

[0013] The present invention further relates to a non-transitory computer readable storage medium storing instructions which, when executed by a computer, cause the computer to execute the indicated method.

[0014] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are intended to provide a further explanation of the present invention, as claimed.

[0015] The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate some of the features of the present invention and together with the description, serve to explain the principles of the present invention.BRIEF DESCRIPTION OF DRAWINGS

[0016] FIG. 1A is a block / schematic diagram of an HVACR system including an electronic edge controller according to an example of the present invention.

[0017] FIG. IB is a process block diagram of a microcontroller that is included in the electronic controller shown in FIG. 1 A and schematically illustrates a microprocessor for storing and executing an indicated controller program, as well as for performing data collection functions, controlling signal generation to one or load devices, and calculating the estimated energy consumption and demand, energy savings, and demand reduction..

[0018] FIG. 1C is a schematic diagram of the serially connected primary relay and secondary relay shown in the electronic controller of FIG. 1 A.

[0019] FIG. 2 is a flowchart depicting a method according to an embodiment of the present invention, wherein the method includes receiving conditioning data, processing the conditioning data using Al software to create an optimized thermostatic control signal, and estimating a baseline energy consumption of an appliance.

[0020] FIG. 3 is a schematic diagram depicting a network including an edge node device in accordance with the present invention.

[0021] FIG. 4 is an electric circuit diagram showing the serially connected primary relay and secondary relay of an electronic controller according to the present invention.

[0022] FIG. 5 is a table showing operations that can be enabled by the system of the present invention and exemplifies various operations for a single control channel for controlling an HVACR or other appliance, wherein the terms “PDR PACE Al” and “ADR PACE Al” in the exemplary circuit diagram shown refer to various forms of the Al control algorithms that are described and defined herein.

[0023] FIG. 6 is a schematic representation of an exemplary embodiment of an Al-enabled thermostatic device for a 4-channel HVAC unit that includes 2-stage cooling and 2-stage heating.

[0024] FIG. 7 is a flowchart depicting a method according to an embodiment of the present invention, wherein the method includes receiving conditioning data for an energy consuming and energy generating system, processing the conditioning data using Al software to create an optimized control signal, and estimating energy consumption, demand, and supply of the system.

[0025] FIG. 8 is a flowchart depicting a method according to an embodiment of the present invention, wherein the method includes receiving conditioning data for a refrigeration system, processing the conditioning data using Al software to create an optimized thermostatic control signal, and estimating a baseline energy consumption of the refrigeration system.

[0026] FIG. 9 is a flowchart depicting a method according to an embodiment of the present invention, wherein the method includes receiving conditioning data for an entire HVACR system, processing the conditioning data using Al software to create an optimized thermostatic control signal, and estimating a baseline energy consumption of the system.

[0027] FIG. 10 is a process block diagram of process control logic of an energy control algorithm, which can be used by an electronic controller for automatic energy control and energy savings estimations for an HVACR system according to an example of the present invention.

[0028] FIG. 11 is a process block diagram of process control logic used to determine a general hysteresis thermostat signal that can be used for heating or cooling, according to an example of the present invention.

[0029] FIG. 12 is a process block diagram of process control logic which applies a time delay to an input signal, i.e., a hysteresis signal determined using the process of FIG. IB, to produce a control signal (“uPace”) which is used in the process of FIG. 1, according to an example of the present invention.

[0030] FIG. 13 is a process block diagram of process control logic for an energy calculation (“Calc energy”) which is used in the process shown in FIG. 1, according to an example of the present invention.

[0031] FIG. 14 is a process block diagram of process control logic of the “Plant Model” in FIG. 1, which models the dynamics of a forced air heating, cooling, or refrigeration system under hysteresis thermostat control, according to an example of the present invention.

[0032] FIG. 15 is a process block diagram of the Plant Model of FIG. 14 during an OFF state, according to an example of the present invention.

[0033] FIG. 16 shows a calculation scheme that can be used to calculate the A parameter during the OFF state, according to an example of the present invention.

[0034] FIG. 17 is a process block diagram of the Plant Model of FIG. 14 during an ON state, according to an example of the present invention.

[0035] FIG. 18 shows a calculation scheme that can be used to calculate the B parameter, according to an example of the present invention.

[0036] FIG. 19 shows an illustration of the use of an Extended Kalman Filter (EKF) to estimate the A and B parameters, according to an example of the present invention.

[0037] FIG. 20 is a process block diagram of the Plant Model with Extended Kalman Filter (EKF) used as a method for estimation, according to an example of the present invention.

[0038] FIG. 21 is an electrical connection diagram for a single stage cooling application using an electronic controller according to an example of the present invention, wherein this configuration is shown as used when a single thermostat is used to control one HVAC cooling device (e.g., a compressor).DETAILED DESCRIPTION OF THE PRESENT INVENTION

[0039] According to the present invention, a method of reducing energy consumption and demand of an edge device appliance network, is provided. The edge device appliance network can be embodied as 1) circuitry and software in original equipment, including 2) thermostatic control devices, and 3) as add-on edge devices, and can include one or more appliances selected from thermostatic devices for HVACR equipment, humidity control devices for HVACR equipment, control devices for power-generating equipment, and for like controls for edge device appliances.

[0040] The method can comprise receiving conditioning data pertaining to a thermostatic or other control output of an edge device appliance, for an HVACR-conditioned space or other load. The conditioning data can comprise, for example, one or more of control inputs, state variable inputs, and sensor inputs. The method can comprise processing the conditioning data, with artificial intelligence (Al) software, to create an optimized thermostatic control signal for the edge device appliance in the conditioned space. The edge device appliance in the conditioned space can be controlled based on the optimized thermostatic control signal. Regardless of whether the present invention (the PACE Al system) is online or offline, wherein for HVACR equipment, “offline” means under OEM thermostatic control, the PACE Al system adaptively estimates the conditioned space thermal capacitance, thermal resistance, and internal load. Using these estimated values a plant model is executed using measured measurement data and an estimated temperature setpoint. The temperature setpoint and conditioned space temperature produced by the plant model is applied to a hysteresis temperature controller model to create an estimated thermostat signal. Energy savings is estimated based on the integrated difference between the estimated thermostat signal produced by the plant model and the measured PACE Al system control signal.

[0041] The method can comprise receiving an energy consumption and / or demand reduction modification command, for example, from a power grid operator, from the Emergency Broadcast System, from a local utility agency, from an emergency agency, from a first responder agency,from a Police agency, or the like. The method can also include a system for generation of continuous energy consumption and / or demand modification control inputs, for continuous optimization of energy and demand. The energy consumption and / or demand reduction modification command can include a command to retrieve modification data. The modification data can comprise a demand reduction command value, a time-of-day energy rate, current weather information, forecast weather information, or a combination thereof. The energy consumption modification command can activate a retrieval of modification data. The modification data retrieved can comprise a demand reduction command value, a time-of-day energy rate, current weather information, forecast weather information, or a combination thereof.

[0042] The method can comprise receiving power consumption data from sensors to determine real-time energy consumption and demand data of an edge device appliance in the HVACR-conditioned space or other load. The method can comprise generating an energy consumption and demand modification signal using Al software. The energy consumption and demand modification signal can be based on the conditioning data, the modification data, and the real-time energy consumption and demand data. The method can further comprise controlling the edge device appliance in the HVACR-conditioned space or other load, based on the energy consumption and demand modification signals. The energy consumption and demand modification signal can comprise a modification or setting of the timing of on and off (ON / OFF) commands so that the appliance achieves a reduction in energy consumption.

[0043] The method can further comprise sending, from the edge device appliance, sensor outputs for updating the optimized thermostatic control signal. The sensor outputs can be outputs used for updating the optimized thermostatic control signal. The sensor outputs can be continuously sent from the edge device appliance.

[0044] The method can further comprise estimating baseline energy consumption data of the edge device appliance in the conditioned space. The estimating can be based on the conditioning data. The energy consumption modification signal can be further based on or adjusted in view of the estimated baseline energy consumption data.

[0045] The present invention also provides an Internet-connected, or building automation system-connected, edge device, that utilizes edge-based and cloud-based artificial intelligence and machine learning (AI / ML) for optimizing energy consumption of appliances. The present invention also provides a control system that includes the edge device. The edge device, or appliance, can include, for example, a thermostatic and other control device for heating, ventilation, air-conditioning, and refrigeration (HVACR) equipment, a control device for powergenerating equipment, a control device for like appliances, or an appliance including a controldevice. A system of one or more different appliances can be controlled by the edge device and control system of the present invention. The AI / ML can be utilized for permanent and commanded electric and / or natural gas or other fuel demand reduction. The control system can work with one to any number of edge devices and can be integrated with existing control and remote monitoring systems. For example, the control system can be integrated into existing or new building automation systems, existing or new industrial process control systems, existing and new electric power and natural gas transmission and distribution networks, and existing or new solar powergenerating appliances.

[0046] At least one edge device of the present invention can comprise a microcontroller, two or more control relays, a multiplicity of input and output connection options for sensors and other input / output (I / O) variables, edge and cloud artificial intelligence / machine learning (AI / ML) software, and other software that includes firmware. Each of the control relays can be a two-state device, having an open state and a closed state, or may have more than one state of, or in, operation. The control relays can be installed in series with existing cooling and heating electrical thermostat or other control lines. The control relays are each actuated by the AI / ML software that controls the state (for example, open or closed) of each. The AI / ML software can also control the duration of either state of each control relay. The number of control relays in a single edge device can be set to any number, limited only by installation space requirements. The edge device can be battery-powered, mains powered, or a combination thereof. The edge device can have a number of other control output options in addition to the control relays.

[0047] The edge device can be used to control one or more appliances. The appliance controlled by the edge device can be, or include, for example, cooling equipment, heating equipment, ventilation equipment, refrigeration equipment, solar or other power-generating equipment, compressed air equipment, or a combination thereof. The appliances can be, or include, for example, a heater, a furnace, a ventilation fan, an air-conditioning unit, a refrigerator, other HVACR equipment, a solar power-generator, a thermoelectric power-generator, a geothermal power-generator, another power-generator, and the like. The AI / ML software can automatically and continuously adjust thermal capacitance and resistance parameters of a dynamic model of the appliance, for example, of the cooling or heating equipment, residing within the AI / ML software. The AI / ML software can utilize a multiplicity of sensor and other input signals that can include supply air or fluid and return air or fluid temperatures, pressures, or humidities, amperages, voltages, proxy signals, other performance and control variables, and combinations thereof.

[0048] The present invention also provides a system of edge devices, which uses cloud-based learning databases of curated data. Each edge device can send data and receive commands in real time from a cloud-based system data consolidation and analytic engine and / or cloud, remote, or cloud and remote command center. The control system can receive location, weather, and weather forecast information, and other information from various Internet and database sources, in real time. Control, state variable, sensor, and other inputs can be processed by the AI / VIL software as training data, creating optimization outputs via the normal control sequence of the operations of one or more of the connected machines. The outputs can represent an additional source of machine learning for a range of applications. The dynamic AI / ML modeling is continuously updated, for example, to produce optimizing thermostatic control signals for heating, cooling, and refrigeration (HVACR) equipment, and solar power-generating equipment.

[0049] The present invention also provides an edge node device for controlling at least one edge appliance in an HVACR system. The edge node device can be configured for sending a control signal to the at least one edge appliance. The at least one edge appliance can have, for example, a thermostat, a power meter, or the like. The edge node device can comprise a controller and an output configured to be in communication with the at least one edge appliance. The controller can comprise a primary control relay and a secondary control relay that is arranged in series with the primary control relay. The primary control relay can comprise a first artificial intelligence (Al) command input whereat commands from an Al cloud and command center can be input to the primary control relay. The secondary control relay can comprise a second Al command input. Both the first Al command input and the second Al command input can be configured to be in communication with an Al cloud and command center for receiving a first Al command signal and a second Al command signal, respectively.

[0050] The primary control relay can have an open state and a closed state. The secondary control relay can have an open state and a closed state. The output can be configured to be in communication with the at least one edge appliance and configured to send a control signal from the controller to the at least one edge appliance. The primary control relay can operate as a 2-state relay, that is, to either be closed or open. When closed, the primary control relay can transmit a thermostat-generated signal as it changes between 24V AC and 0VAC. The signal voltage of 24 VAC can generally be for a call for cooling or heating for a respective control channel. The signal voltage of 0VAC can be sent if a demand is satisfied and thus the control signal stops. When open, the primary control relay shifts to its second state of connecting-in a 24V AC continuous voltage signal, that is, a continuous "call" signal.

[0051] The edge node device can be configured such that when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the first secondary control relay, and the output of the primary control relay is used as the Al command signal. The edge node device can be configured such that when the state of the primary control relay is closed, the output of the primary control relay is a thermostat command signal generated by the thermostat, power meter, or the like. The edge node device can be configured such that when the state of the first secondary control relay is closed, the output of the primary control relay is sent to the Al command center to be processed by one or more Al control algorithms and form a processed signal that is passed to the at least one edge appliance as the control signal. The edge node device can be configured such that when the state of the first secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay.

[0052] The present invention also provides a network comprising an edge node device as described herein, and an Al command center. The Al command center can be configured to generate the first Al command signal, the second Al command signal, or both. One or more different Al command centers can be used to generate the first Al command signal, the second Al command signal, or both.

[0053] The Al command center can be remote from the edge node device or local, or a combination of remote and local processing. The Al command center can be in wired communication with the controller. The Al command center can be a cloud-based Al command center. The Al command center can be remote from the edge node device and can communicate with the edge node device via cloud-based computing and communication.

[0054] The edge node device can be configured for sending a plurality of control signals to a respective plurality of edge appliances. Each of the edge appliances can have, as examples, a thermostat, a power meter, a humidistat, or the like. The controller can comprise a primary control relay and a plurality of secondary control relays. Each of the secondary control relays cane respectively be arranged in series with the primary control relay. Each of the secondary control relays can comprise a respective second Al command input. Each of the second Al command inputs can be configured to be in communication with the Al command center for receiving a respective second Al command signal. Each of the secondary control relays can have an open state and a closed state.

[0055] The system of the present invention can utilize hybrid edge and cloud technology, whereby some processing occurs at the edge node device and some processing occurs in thecloud, for example, at a remote Al command center. Continuous cellular or other internet of things (loT) cloud communication and data uploads can be made.

[0056] The controller can have a plurality of respective outputs each being configured to be in communication with a respective one of the plurality of edge appliances. The controller can be configured to send a respective control signal from the controller to the respective edge appliance. The edge node device can be configured such that, for each respective edge appliance of the plurality of edge appliances, one or more of the following rules applies. Rule 1 is that, when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the respective secondary control relay, and the output of the primary control relay is used as the Al command signal. Rule 2 is that, when the state of the primary control relay is closed, the output of the primary control relay is a thermostat or meter command signal generated by the thermostat or meter. Rule 3 is that, when the state of the respective secondary control relay is closed, the output of the primary control relay is sent to the Al command center to be processed by one or more Al control algorithms and form a processed signal that is passed to the respective edge appliance as the control signal. Rule 4 is that, when the state of the respective secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay. The present invention also provides a network comprising an edge node device as described herein, and an Al command center. The Al command center can be configured to generate the first Al command signal, the second Al command signal, or both.

[0057] Also provided by the present invention is a system comprising an edge node device as described herein, an edge appliance such as an HVACR appliance as described herein, a current sensor, and an air duct temperature sensor. The current sensor can be configured to send a sensed current signal to the controller, indicative of a current load being used by the appliance. The air duct temperature sensor can be configured to send a sensed air duct temperature signal to the controller, indicative of the air temperature in ductwork through which air or liquid is moved by the HVACR appliance. The system can include a liquid conduit temperature sensor configured to send a sensed liquid temperature signal to the controller, indicative of a liquid travelling through a conduit connected to the HVACR appliance. The controller can be configured to send data pertaining to a sensed current signal, a sensed air duct temperature signal, a sensed liquid temperature signal, or the like, to the Al command center. The system can further include an Al command center.

[0058] The system can further comprise a cloud-based learning database of curated data. The Al command center can be in communication with and configured to retrieve data from the cloud-based learning database of curated data.

[0059] Al system control inputs, state variable inputs, sensor inputs, and other inputs are utilized by the control system of the present invention to produce estimations of equipment and system baseline, for example, baseline that represents non-optimized operation, including baseline energy consumption and demand, and the like. The AI / ML software can accept a scalable demand reduction command value from any source, for instance, from an electric grid operator or from an electric grid demand response service provider. The AI / ML software can process the demand reduction command and utilize it with the baseline energy consumption into control relay signals that produce a desired demand reduction percentage. Sensor data from current transducers, voltage sensors, or other inputs are converted, via the control system of the present invention, to real time current, kilowatt, kilowatt-hour, therm, British Thermal Unit (BTU), BTU / hour, and other consumption data. The consumption data can be utilized in determining a desired demand reduction percentage.

[0060] The control system can generate control commands that can produce continuous and permanent reductions in energy consumption for the connected edge appliance or system of appliances. A portion of the energy consumption reduction, or savings, can vary in proportion to the estimated time-variant capacitance and resistance values of a given equipment and load system. Another portion of the savings can be based on variable-based algorithms for weather and weather forecast data. Another portion of the savings can be based on schedule-oriented optimization and in adding optimization rules to. Optimization rules can be, for example, that an HVACR unit is not to run in a vapor compression air conditioning mode or in a heating mode if the sensed return air or fluid temperature is within a specific range, for example, (>) (<) X. In this way, the control system can be used to accomplish the aims of HVAC unit economizer operation, without the expense of an economizer.

[0061] The control system of the present invention and its AI / ML software can provide a customizable set of variables for building operators or industrial process control operators, wherein a user-controllable “slider” can be applied. The slider can provide, at one end, more energy savings and / or demand reduction, and at the other end, more operation that is characteristic of native control architecture operation. The adjustable slider can thus provide more assurance to an operator of an “as normal” comfort while at the same time assuring that performance variables are achieved.

[0062] The AI / ML software can calculate optimized starts per hour for HVACR and other equipment, using library databases of OEM recommendations and sensed data. The AI / ML software can provide limits on equipment starts to ensure compliance with equipment manufacturer specifications. The AI / ML software can enforce anti-short-cycle and other machine protections. The control system of the present invention can also use the above-mentioned variables to detect and correct anomalies in HVACR and process unit operation, beyond anti- short-cycling and maximum starts per hour.

[0063] The control system can incorporate a number of fail-safe features, including that in the event of a power failure to the edge device or, under certain circumstances, to the cloud-based data analytic and command centers, all control relays or other control outputs will close, yielding control back to the normal (non-optimized) equipment inputs. The control system can detect and provide alerts on potential tampering with both the control system and the equipment to which it is attached, for example, an alert stating or displaying a message such as “connected, not following commands, potentially unwired”. The control system can detect, alert on, and record electric power grid and microgrid voltage and frequency drops and other power quality issues, that can be used as documentation in the event that there is equipment damage caused by such issues.

[0064] The control system can utilize the inputs described above to conduct optimized precooling or pre-heating of a building. For training and input / output data, the control system can use the output (PACE_CH*) signals to continue cooling past the input OEM (0EM_CH*) signal, until a specific return air temperature setpoint is reached. The control system can also improve performance of HVACR systems that are oversized, or undersized, for the cooling or heating load to which they are connected, and where the native control architecture is delivering sub-optimal energy efficiency.

[0065] The control system can be configured to work with OpenADR 2.0 and OpenADR 3.0 demand reduction and price signal inputs, with the latter including Time of Use (TOU) and forecast electric price signals. The control system can use these inputs to dynamically curtail HVACR operation in a powerful way, and can serve as an easily -deployable, low-cost OpenADR Virtual End Node (VEN).

[0066] In addition to dynamic optimized “smart pre-cooling” and “smart pre-heating” as described above, the control system can also perform 24 / 7 / 365 enhanced energy consumption and demand reduction using, for example, a current sensor and / or OEM_CH* input directly in real time, with a training routine that would involve, e.g.: TRAINING DATA: PACE_CH*::TEMP*, MIN(kWh). A similar applications would use dynamically sensed HVACR air conditioning and heating supply and return air temperature or supply and return hydronic heating water, to increasesupply temperatures while maintaining return temperatures, for both energy consumption and demand reduction at a unitary, meshed building, or microgrid level. In doing so, the control system can utilize the PACE_CH*, TEMP* and other data in machine learning for machine learning. Examples of such utilizations are provided, for instance, at blog.research.google / 2023 / 12 / advancements-in-machine-leaming-for.html.

[0067] The control system edge device can incorporate a “virtual inverter” drive for electric motor optimized operation, that does not require or need power-side connections of inverter drives that are required in current practice.

[0068] There is tremendous unmet demand for tenant-side solutions to building energy, energy cost, and carbon reductions. At the same time, building owners have unaligned incentives to provide these reductions at an extra cost, when it is the tenants that bear the energy cost. Building owners are further disinclined to provide such measures as their operating revenues are being threatened by work from home and the continued erosion in brick-and-mortar retail traffic. The control system can be combined with and integrate with other control and monitoring measures, including circuit breaker-panel monitoring and energy storage, to create a portable building automation system that, for example, office building tenants and retail building tenants could install and can take with them when they vacate the premises.

[0069] The control system can utilize its detailed input data streams to produce machine alerts and diagnostic reports, including, for example, for work tickets, for machine repair, for preventive and predictive maintenance, and the like, and also to remedy certain machine issues. For example, in the event that an HVACR unit is mis-starting on one of its compressors, due to an electrical short-circuit, the control system can utilize its detailed input data streams to produce machine alerts and diagnostic reports related to the mis-starting. The present invention also provides a machine alerts and diagnostic report service, for example, bundled in combination with a subscription, or at per-event pricing. Carryover of yearly credits can be provided.

[0070] The control system can be used in combination with a photovoltaic (PV) solar array. The control system can utilize a real-time utility interval electric meter or PV solar inverter signal, or other signals including lumen detection for cloud cover at the PV solar array, to be able to dynamically reduce HVAC building energy usage in order to maintain a higher level of reliability for dispatchable net generation to the power grid, from the PV solar array.

[0071] For appliances or machines that utilize natural gas or other flammable substances, the control system can incorporate an easily added intelligent emergency shutoff at the machine unit, that can be interior or exterior to the building. The shutoff enables a great risk mitigation addition for such appliances, especially against risks from fire, earthquake, tornado, or industrial accident.

[0072] The control system can be offered as an open-source Al product and service bundle, similar to the examples as described at www.cbinsights.com / research / open-source-ai- development-market-map / ?utm_source=CB-i-Insights-i-Newsletter&utm_campaign=73a0a645f0- newsletter_general_sat_2023_ll_04&utm_medium=email&utm_term=0_9dc0513989- 73a0a645f0-87700161.

[0073] The control system can provide auto-detection and correction on a loss of connectivity, with alerts, and hardened reset and regain-connectivity software.

[0074] To address cybersecurity concerns in both power grid operations and building systems, the control system can provide monitoring and alerts. The control system can include a hardened cloud cellular connection outside of standard wired networks and potentially vulnerable firewalls. Via the cloud cellular connection, the control system can provide monitoring and alerts to improve the resilience to adversarial attacks, especially improved with respect to the resilience of wired networks to adversarial attacks.

[0075] The control system can include two or more implementation modes in order to gain the maximum uptake for the ability of a technology to reduce carbon and cost, in particular where Internet connectivity may be problematic. For example, the following two modes, Basic and Professional, can be provided for small HVACR units such as small apartment systems, as opposed to larger commercial units that can be readily Internet-connected. For the “Basic” mode, the control system can be non-connected or periodically connected, can learn locally, can use open source software and / or software protected by patent coverage, can be provided with savings warranty and other insurtech and PdM services, and can periodically receive over the air (OTA) software updates, and can periodically or continuously receive edge device AI / ML and other updates. For the “Professional” mode, the control system can be continuously connected to receive periodic OTAs but can provide greater control and features, and continuous monitoring and learning, while also providing savings warranty and expanded insurtech and PdM services.

[0076] The control system can be integrated with Internet-connected thermostatic control devices, for example, with an additional wireless edge module at the HVACR controls, and / or integrated with building automation systems. The edge device of the control system can be designed to function as a smart loT gateway, with uses of smart loT gateways in mobile loT and logistics, industrial processes, smart agriculture, water and wastewater management, and other fields.

[0077] The control system can monitor and manage refrigerant systems. The control system can utilize supply and return refrigerant temperatures, pressures, and other data to provide refrigerant-loss detection and analytics. The refrigerant monitoring and management services canbe easily deployed and can be provided at a low cost and can be, for example, subsidized by AI / ML energy cost savings.

[0078] The control system can include added sensors in HVACR ducting and unitary equipment to provide easily added monitoring and control of building indoor air quality (IAQ). Since building IAQ can also include monitoring for and control of viral and other pathogens, the control system can be integrated with duct-level or elsewhere-placed ultraviolet disinfection devices.

[0079] The present invention provides an automated control of on / off heating, cooling, and refrigeration equipment under closed loop temperature and / or humidity control via hysteresis thermostat. This invention uses parameter estimation methods described herein to estimate parameters of a dynamic plant model of the hysteresis thermostat, the conditioned space, and the cooling, heating, or refrigeration equipment and then uses the model to create an equipment control signal that achieves a desired level of energy savings.

[0080] Energy savings can be achieved according to the present invention by using any one or a combination of many methods. The methods that can be used can include: (1) applying preheating or pre-cooling using time of day energy costs; (2) applying pre-heating or pre-cooling based on forecasted weather information or data; (3) applying a plurality of equipment "on" time values based on a demand response command signal; (4) reducing the number of thermostat "on" calls if a device is estimated to be oversized; (5) modulating thermostat "on" calls to prevent temperature overshoot and undershoot, (6) modulating thermostat "on" calls to reduce "run on" time, and (7) modulating thermostat “off” calls to increase off times. For each of the seven methods, (l)-(7), a real time energy savings estimate is calculated based on the integrated difference between the estimated thermostat signal from a plant model as described herein, and the system control signal being applied. The control signal can come from the exemplary PACE Al system described herein.

[0081] Time of day energy cost control methods utilize historical temperature setpoint information and "time of day energy rate" information passed to the control system from the cloud. The control system can be, for example, the exemplary PACE Al system described herein. The energy rate information can be forecasted energy rate information, real time energy rate information, or both. When the "time of day energy rate" information is forecasted, additional controls can be utilized, for example, by the PACE Al, for pre-cooling and pre-heating a conditioned space, based on a known (forecasted) cost of energy. When the "Time of day energy rate" information occurs in real time, additional controls can be utilized by the system, forexample, by the PACE Al, to alter the conditioned space setpoint temperature to a pre-determined or user specified value.

[0082] Weather control methods utilize historical temperature setpoint information and weather information passed to the PACE Al from the cloud. The weather information can be forecasted weather information, real-time weather information, or both. Regardless of whether the weather information is forecasted or real time, additional controls can be provided, for example, by the PACE Al system described herein, to modulate the equipment ON time to achieve further energy savings based on forecasted and / or real time weather information.

[0083] Demand Response control methods utilize a signal from the utility to reduce electric demand during periods of peak load on the utility infrastructure. The reduction value can range from 1 (turn off all energy consumption devices) to 0 (no action necessary). Algorithms, for example, provided in the exemplary PACE Al system described herein, can provide control to meet a plurality of demand reduction values, for example, ranging from 0 (equipment off) to 1 (equipment on). As an example, if a 25% reduction is specified, the algorithm can modulate a secondary relay as described herein to achieve a 25% reduction of an existing power consumption.

[0084] Oversized equipment control methods utilize and compare the equipment "on" time and the number of cycles per time unit to establish a dynamically varying metric indicating that the equipment is oversized. Depending on the metric, the thermostat "on" call signal can be decimated every x-cycles (x being a variable) creating fewer "on" cycles and increased "on" time durations until the oversize metric is eliminated.

[0085] Overshoot and Undershoot Prevention control methods utilize measured data and the respective plant model to predict the amount of time that the thermostat "on" signal should be truncated to prevent overshoot or undershoot of the conditioned space temperature.

[0086] Reduced on time control methods measure the conditioned space temperature rate of change while the equipment is running, using both measured values and the respective plant model. The thermostat signal is forced Off when the temperature rate of change reaches a threshold value. The thermostat control then responds normally as the conditioned space temperature either increases (for cooling) or decreases (for heating).

[0087] Increased off time control methods utilize measuring the conditioned space temperature rate of change while the equipment is not running, using both measured values and the respective plant model. The thermostat signal is held off until the conditioned space temperature exceeds a threshold value. Once this occurs, the thermostat control responds normally turning on for both cooling and heating.

[0088] The foregoing seven methods, (l)-(7), can be used individually, independently of one another, all together, or in any combination thereof.

[0089] The present invention provides an electronic controller that can be used to implement the indicated methods for estimating parameters of duty cycled HVACR equipment and the energy savings that can be obtained with use of adjusted control signals generated according to the present invention. The indicated controller can be implemented as part of a retrofittable electronic controller add-on device that includes integrated programs that can automatically and optimally calculate and control execution of duty cycles and cycle time durations for heating equipment, cooling equipment, and / or refrigeration equipment that are controlled to specific thermodynamic and electrical demand levels. The add-on electronic controller can be installed in series in one or more thermostat control signal lines, which is capable of intercepting thermostat signals before they reach an intended load unit of an HVACR system. The electronic controller can apply an algorithm to OEM signals and behavior thereof to generate an output signal for the load unit that can replace (or allow) the original control signal, to provide a selected energy savings level in the system. The controller can be implemented as a computer program stored in a memory device and executable with a microprocessor embodied by the electronic controller. The program can provide a signal processing algorithm. The electronic controller can include signal generation capability to output control signals from the electronic controller to the load unit. The electronic controller can be readily retrofitted into an existing HVACR system or incorporated into a new HVACR system.

[0090] FIG.1 A shows an HVACR system 11 including an electronic controller 18 on which the indicated controller program or programs can reside or be retrieved and from which the program can be executed for signal processing and generation. Electronic controller 18 comprises a primary relay 30 and a secondary relay 50. Electronic controller is in communication via, for example, a receiver and transmitter, with a cloud-based command center 70. Cloud-based command center 70 is configured to run artificial intelligence (Al)Zmachine learning (ML) software and, based on data that is received and / or input, provide a control signal or Al command to electronic controller 18. Electronic controller 18 can then control load unit 20, an edge device appliance, based on the Al command. Alternatively, electronic controller 18 can, instead, send a different command to load unit 20, for example, from a thermostat command.

[0091] Electronic controller 18 can be retrofitted in system 11 to provide control of at least one HVACR load unit 20 that provides condition control in a zone 2. Power line 10 passesthrough utility meter 12 at the structure, wherein at least one load unit 20 to be controlled is located. Meter 12 measures usage and demand of electrical energy at that location. Load unit 20 can be, for example, an air conditioner, heat pump, furnace, refrigerator, boiler, or other load unit of an HVACR system.

[0092] Operative main power line 10 generally is unconditioned and supplies operative power to load unit 20 via load control switch 26, such as a relay, and typically other load units and appliances in the same structure (not shown). Power supply line 10 can be, for example, a 110 volts alternating current (VAC), or 220 VAC, or other mains power supply line powering HVACR system 11 to be retrofit with electronic controller 18. For system 11 to be retrofit, at least one standard thermostat 14 can be connected to the HVACR load unit 20. Thermostat 14 can be connected via line 13 to power line 10. To simplify this illustration, a step-down transformer, such as a 24-volt transformer, that can be used in powering the thermostat from power line 10, is not illustrated in this figure. A step-down transformer is, however, illustrated in the wiring diagram shown in FIG. 21.

[0093] Electronic controller 18 also uses direct inputs from a dedicated temperature sensor 22 to operate and function as designed. Temperature sensor 22 can be an outside temperature sensor, an inside temperature sensor, or both. The temperature sensor can be located remotely from thermostat 14. Thermostat 14 can be located inside the building or structure having a space to be temperature conditioned. Electronic controller 18 and temperature sensor 22 can communicate via hardwire or wireless communication line 17. The temperature sensor can be a physically separate component from the electronic controller or alternatively can be integrated with the electronic controller, for example, as is convenient when the electronic controller is located outside.

[0094] Electronic controller 18 is not directly powered from power line 10, and it does not need to be. Electronic controller 18 is powered by the thermostat signaling intended for the load device(s). Electronic controller 18 typically is electrically dormant (or inactive) or sleeps with respect to its signal processing features until receiving / intercepting an ON signal from the thermostat, and then electronic controller 18 becomes awakened (active) to apply a program as part of an algorithm such as shown herein for signal control processing and control signal generation to the intended load device(s).

[0095] In one typical situation, a control signal line 15 of thermostat 14 can transmit an AC voltage of 24 volts during the periods when a thermostatic control is, for example, calling for cooling from an air conditioning unit (load unit), or heating from an electric furnace, and so forth. The control signal would normally activate load control switch 26 in main power line10 to power the load unit 20. That is, in the absence of electronic controller 18, control signal line 15 would be in control of opening or closing load unit control switch 26, and thereby opening or closing the circuit of operative power line 10 and controlling the flow of operative power to load unit 20. Electronic controller 18 is interposed and installed in the thermostat control signal line 15 in series at some point between thermostat 14 and the load unit control switch 26. As shown, thermostat line 15 can be cut and connected at one cut end to electronic controller 18. As also shown, the remaining portion of the cut signal control line, referenced as line 24, can be connected at one end to electronic controller 18 and at the other end to load control switch 26.

[0096] Electronic controller 18 can be physically mounted, for example, in sheet metal (not shown) near the load unit 20, such as a standard sheet metal construction enclosure used with the load unit. Preferably, the tapping of electronic controller 18 into the control signal line 15 (24) is made as close as practically feasible to the load control switch 26. Usually, it can be possible to make the connection within the physical confines of the load unit itself. The connection of electronic controller 18 in the control signal line could be made, for example, within the casing containing the compressor unit of a residential air conditioning unit. For example, electronic controller 18 could be mounted in a sheet metal enclosure that houses the OEM controls for a compressor of an air conditioning unit as installed on a slab or platform near ground level immediately adjoining a home or building supported by the unit, or on a rooftop thereof.

[0097] Electronic controller 18 can include on-board user interface controls 19 and / or can receive control inputs and / or parameter data 23 from a remote input device 21, which can be further understood by other descriptions herein that will follow. The input device 21 can be “remote” in the sense that it is a physically separate device from electronic controller 18, which can communicate with the controller, such as via an attachable / detachable communication wire or cable link or a wireless communication link. The remote input device 21 can be an electronic service tool for the controller, a laptop computer, a desktop computer, a tablet computer, a smartphone, or other device. Temperature sensor 22 can be located as a separate unit, or as integrated with the controller (if also located outside), near a compressor of an air conditioning unit or other load unit to be controlled as installed on a slab or platform near ground level immediately adjoining and outside a home or building supported by the unit, or on a rooftop thereof, or located elsewhere in the close vicinity of the home or building supported by the unit.

[0098] In operation, electronic controller 18 receives electrical flow over control signal line 15 based on a thermostat control signal intended for powering up load unit 20, and electroniccontroller 18 can immediately awaken to intercept the thermostat signal and initiate its suite of control programs before an output control signal is sent from electronic controller 18 to load unit switch 26. As indicated, the output control signal may be a replacement signal for the OEM signal or the OEM signal, depending on the outcome of the running of the controller’ s algorithm.

[0099] Thermostat 14 is preferably configured or preconfigured to generate only an ON / OFF signal, by which the air conditioner / heat pump compressor, furnace, or other load unit is turned on / off. Preferably, the thermostat 14 used in the system 11 is designed to provide ON / OFF control at a load unit to turn the load unit completely on or completely off. When the thermostat is an ON / OFF control device, the thermostat can decide if the output needs to be turned on, turned off, or left in its present state. ON / OFF control by an OEM thermostat typically comprises selecting a set point, and a native or default OEM deadband may apply or may be selected by a user, that straddles the set point.

[0100] Temperature sensor 22 can be located near locations of the load unit 20 outside the structure including at least one space that is being temperature-controlled. Temperature sensor 22 can be a remote temperature sensor. Temperature sensor 22 can be a sensing module that can be plugged via integral multiple prongs into an outside electrical outlet accessing the power line 10 and / or can be battery powered. Temperature sensor 22 can be part of a device that is directly plugged into an outside outlet or can be connected to an outlet as a module to the outlet via a power cord or power extension cord. The outside temperature sensor may be included in a battery powered unit.

[0101] Though FIG.1A shows a single control line 15 cut and connected from a single thermostat 14 and connected to electronic controller 18, for simplification, it will be appreciated that in single or dual thermostat configurations, multiple control lines from a single thermostat, or a single control line from each of multiple thermostats each can be cut and separately connected to electronic controller 18, such as different respective input pins of the electronic controller. Where electronic controller 18 controls more than one load device, an output signal control line can be connected at one end to electronic controller 18 and at the other end to the load control switches of each load device. For simplification, only one load unit 20 under the load control and management of electronic controller 18, and a single control signal line, are shown in HVACR system 11 of FIGS. 1 and 14, it is to be understood that HVACR system 11 can include multiple individual loads under thermostat control, for example, multiple compressors, or a compressor unit and a blower unit, and other similar or diverse loads, depending on the configuration.

[0102] As indicated, the electronic controller of this invention can be wholly connected in the control lines of individual subloads of the equipment. In other words, an air conditioner may have a separate control line for the subloads of the compressor unit and the blower unit. The electronic controller can be used to control either one or both of these subloads. The overall power line to all the subloads of the air conditioning unit is generally not in any way altered by the electronic controller of this invention. Further, the usual conventional electrical grounding means is not shown in the schematic diagram of FIG.1A as it is not a matter of particular concern in this invention.

[0103] Electronic controller 18 of FIG.1A can be implemented, for example, in a standalone configuration or in a networked configuration. A stand-alone configuration can be used, for example, in a single load unit residential application (e.g., < about 5 ton HVACR load unit). A networked configuration can be used, for example, as part of a building management system (BMS) for providing HVACR in a larger scale applications, such as higher energy use / demand residential, commercial, or industrial buildings or equipment, and the like, or, as a network of electronic controllers, each attached to a dedicated load unit.

[0104] Electronic controller 18 in FIG.1A includes at least one microprocessor operable to receive thermostat input signals, apply the indicated programs to thermostat signals received, and transmit an output signal under the command of the microcontroller to the HVACR load unit to be controlled.

[0105] As shown in FIG. IB, a microcontroller 183, which is included in electronic controller 18 in FIG.1A, can include, for example, a microprocessor for storing and executing the indicated the indicated controller program, as well as performing data collection function, controlling signal generation to the load device(s), and calculating the estimated electrical savings. As shown in FIG. IB, microcontroller 183 can include a microprocessor 1832, a computer-readable storage medium 1833 shown as incorporating memory 1835, which all have been integrated in the same chip. Microprocessor 1832, also known as a central processing unit (CPU), contains the arithmetic, logic, and control circuitry needed to provide the computing capability to support the controller functions indicated herein.

[0106] The memory 1835 of the computer-readable storage medium 1833 can include nonvolatile memory, volatile memory, or both. Computer-readable storage medium 1833 can comprise at least one non-transitory computer usable storage medium or memory storage device. The non-volatile memory can include, for example, read-only memory (ROM), or other permanent storage. The volatile memory can include, for example, random access memory (RAM), buffers, cache memory, network circuits, or combinations thereof. Thecomputer-readable storage medium 1833 of the microcontroller 183 can comprise embedded ROM and RAM. Programming and data can be stored in computer-readable storage medium 1833 including memory 1835. Program memory can be provided, for example, for the energy control algorithm controller program 1838, which includes such as the energy control main program 1836, plant model controller program 1837, delay calculation controller program 1831, and energy calculation controller program 1839, and as well as store menus, operating instructions and other programming such as indicated herein, parameter values and the like, for controlling electronic controller 18. These programs can be stored in ROM or other memory.

[0107] In combination, the programs provide an integrated control program 1838 residing on electronic controller 18. Data memory, such as FLASH memory, can be configured with data parameters. Memory can be used to store data acquired that is related to the operation of a load device to be controlled, such as thermostat command on times and calculated off times. The microprocessor 1832 and memory 1833 can be integrated and supported on a common mother board 1830, or the like, which can be housed in an enclosure (not shown) having input and output connection terminal pins, a communication link / interface connector port(s) (e.g., a mini-, or micro- or standard-size USB port for receiving a corresponding sized USB plug), and the like, which are discussed further with respect to FIG. 21.

[0108] Microcontroller 183 can be, for example, an 8 bit or 16 bit or larger microchip microprocessor including the indicated microprocessor, and memory components, and is operable for input and execution of the indicated demand regulator controller program, and other included programs. Programmable microcontrollers can be commercially obtained to which the control program indicated herein can be inputted to provide the desired control. Suitable microcontrollers in this respect include those available from commercial vendors, such as Microchip Technology Inc., Chandler, AZ. Examples of commercially available microcontrollers in this respect include, for example, the PIC16F87X, PIC16F877, PIC16F877A, PIC16F887, dsPIC30F4012, and PIC32MX795F512L-801 / PT, by Microchip Technology, Inc.; Analog Devices ADSP series; Jennie JN family; National Semiconductor COP8 family; Freescale 68000 family; Maxim MAXQ series; Texas Instruments MSP 430 series; and the 8051 family manufactured by Intel and others. Additional possible devices include FPGA / ARM and ASIC's. The demand regulator controller program indicated herein can be inputted to the respective microcontrollers using industry development tools, such as the MPLABX Integrated Development Environment from Microchip Technology Inc.

[0109] Though electronic controller 18 is illustrated in FIG. 1 A as a stand-alone unit tapped into the thermostat signal line 15 (24) to the load unit to be controlled, the indicatedmicroelectronics of the controller optionally may be incorporated and integrated into the thermostat unit or a Building Management System (BMS). An algorithm incorporating the demand regulator controller program, and other indicated control programs and features of the electronic controller, can be added to native thermostat signal control software of the thermostat. An algorithm incorporating the demand regulator controller program, and other indicated control programs and features of the electronic controller, can be added to Building Management System (BMS) software where a BMS provides control to the load unit or units of the HVACR, eliminating a need for a physically separate electronic controller. In the combined thermostat / electronic controller arrangement, the interception of the OEM thermostat signal and processing thereof by the controller microelectronics can occur at the modified thermostat unit without the need for a physically separate microelectronic controller being tapped into the thermostat signal line 15 (24) between the thermostat and the load unit to be controlled.

[0110] FIG. 1C is an enlarged and more detailed view of the serially-connected primary relay and secondary relay shown in the electronic controller of FIG. 1A. FIG. 1C exemplifies a two-relay embodiment for an edge node, installed on a 1-channel (single-stage) HVAC unit. Such an embodiment can be used, for example, on a small heat pump or small air conditioning unit. As shown in FIG. 1C, primary relay 30 includes a number of terminals for wired connections to various inputs and outputs. Terminal 32 is configured for connection to thermostat command signal that is input to channel 1 of primary relay 30. The connection between the thermostat command signal transmitter and terminal 32 can be wired or wireless. The thermostat command signal transmitter can be located, for example, at the thermostat.

[0111] Terminal 34 is configured for a wired connection to a 24-volt AC output and outputs 24-volts via a wire 38 to terminal 36 where primary relay 30 receives a constant source of 24 volts. Wire 38 also provides a constant source of 24 volts to terminal 52 at secondary relay 50.

[0112] Primary relay 30 also includes a terminal 40 at which an Al command signal is received at primary relay 30, for example, from a command center. The Al command signal can be transmitted via cloud computing via cloud-based command center 70 shown in FIG. 1A. The connection between cloud-based command center 70 and terminal 40 can be a direct, wireless transmission, can be through a pathway that includes an intermediate receiver that is wired to terminal 40, using an intermediate BLUETOOTH® device, or the like.

[0113] Terminals 42 and 44 in FIG. 1C are for wired connections to direct current (DC) circuit. In the arrangement shown, terminal 42 is configured to be connected to the negativeterminal of a DC source, and terminal 44 is configured to be connected to the positive terminal of the DC source.

[0114] Secondary relay 50 includes a terminal 60 at which an Al command signal is received at secondary relay 50, for example, from cloud-based command center 70. The Al command signal can be transmitted via cloud computing via cloud-based command center 70 shown in FIG. 1A. The connection between cloud-based command center 70 and terminal 60 can be a direct, wireless transmission, can be through a pathway that includes an intermediate receiver that is wired to terminal 60, using an intermediate BLUETOOTH® device, or the like.

[0115] Terminals 62 and 64 in FIG. 1C are for wired connections to direct current (DC) circuit. In the arrangement shown, terminal 62 is configured to be connected to the negative terminal of a DC source, and terminal 64 is configured to be connected to the positive terminal of the DC source.

[0116] Secondary relay 50 also includes a terminal 54 for outputting a control signal to a load unit, for example, a compressor, on channel 1. Terminal 56 is not used in the embodiment shown but can be used for outputting a signal, outputting data, sending error messages to cloudbased command center 70, and the like functions.

[0117] FIG. 2 is a flowchart 200 of a method of an embodiment of the present invention. The flowchart 200 includes receiving conditioning data comprising at least one of control inputs, state variable inputs, and sensor inputs, and processing the conditioning data using Al software to create an optimized thermostatic control signal for appliances of a conditioned space, and to estimate baseline energy consumption of the appliances 210. The conditioning data can be received by the edge device or another thermostatic control device. Control inputs can include temperature control, thermostatic setpoints, humidity control, and the like. State variable inputs can include thermal capacitance and resistance parameters, size of conditioned space, estimated energy consumption of appliances, and the like. Sensor inputs can include sensed temperature, sensed humidity, sensed power consumption, and the like.

[0118] The edge device and / or thermostatic control device then controls the appliances based on the optimized thermostatic control signal and continuously sends sensor output for updating the thermostatic control signal 220. The sensor outputs can include the sensed temperature, sensed humidity, sensed power consumption, and the like. The sensor outputs are sensed while the edge device and / or thermostatic control device is controlling the appliances, and thus the optimized thermostatic control signal can be continuously updated and improved.

[0119] The flowchart 200 further includes receiving an energy consumption modification command, wherein the energy consumption modification command includes and / or activatesa retrieval of modification data comprising a demand reduction command value, a time of day energy rate, and / or weather information 230. The energy consumption modification command can be sent to the edge device and / or the Al command center. As an example, the energy consumption modification command can be generated by a computing device of a third party, such as a power / electric grid operator. The energy consumption modification command can include the demand reduction command value that can be a specific percentage requested for power reduction. Alternatively, the energy consumption modification command can be generated by a computing device of an operator of the edge device and / or thermostatic control device, including a request for power reduction of the appliances. The edge device and / or Al command center can retrieve a time of day energy rate and / or weather information. The time of day energy rate can provide data indicating current and forecasted peak load times, while the weather information includes data of current and forecasted weather.

[0120] The edge device and / or Al command center then receives power consumption data from sensors to determine real-time energy consumption of the appliances 240. The edge device and / or Al command center then generates an energy consumption modification signal using Al software, the energy consumption modification signal being based on the modification data, the conditioning data, the baseline energy consumption, and the real-time energy consumption data 250. If the energy consumption modification signal is generated by the Al command center, the Al command center can wirelessly send the energy consumption modification signal to the edge device. The edge device can then control appliances based on the energy consumption modification signal, wherein the energy consumption modification signal comprises a modification of timing of ON / OFF commands for the appliances to achieve energy consumption modification 260.

[0121] As an option, the edge device can control the appliances based on the energy consumption modification signal on a permanent basis. As an option, the present invention can be controlled such that the edge device can revert back to and control the appliances based on the optimized thermostatic control signal. For example, the edge device can receive an end command for energy consumption modification 270, either from a computing device of the operator or by a third party, such as the remote power / electric grid operator. Once the end command is received, the edge device and / or thermostatic control device controls the appliances based on the optimized thermostatic control signal and continuously sends sensor outputs for updating the optimized thermostatic control signal 220.

[0122] FIG. 3 is a schematic illustration of a system 302 according to an embodiment of the present invention. System 302 includes Al edge device 310, a thermostatic control 320,and sensors 330 that feed data to Al edge device 310 and thermostatic control 320. Thermostatic control 320 and / or Al edge device 310 control an HVACR device or system of devices 360, for example, conditioning appliances for conditioning a conditioned space. As shown in FIG. 3, a smart device 340, for example, a smart phone, wirelessly communicates with the thermostatic control 320 and / or Al edge device 310, either directly, for example, via BLUETOOTH® or another closer range wireless connection, or over the Internet 350 or a similar network. BLUETOOTH® is a registered trademark of BLUETOOTH SIG, INC., of Kirkland, Washington, a corporation of Delaware. Smart device 340 receives data from Al edge device 310 and thermostatic control 320 that can be displayed to an end user.

[0123] Al edge device 310 is in communication with a remote Al command center 370 and a power grid operator 380. Each communication can be independent of the other and can be, for example, over Internet 350. In an example, remote Al command center 370 can receive data, generate command signals, and send the command signals to Al edge device 310. Power grid operator 380 can send an energy consumption modification command that includes a demand reduction command value. The demand reduction command value can be a request for a specific percentage of power reduction. The demand reduction command value is sent to Al command center 370 and Al command center 370 generates an energy consumption modification signal using Al software. Al command center 370 then sends the modification signal to the Al edge device 310 over Internet 350, and Al edge device 310 then controls HVACR appliance 360 based on the modification signal. Alternatively, the demand reduction command value can be sent directly from power grid operator 380 to Al edge device 310. Al edge device 310 can then generate the modification signal and control HVACR appliance 360 based on the modification signal.

[0124] FIG. 4 is a diagram of an electric circuit showing a two-relay circuit for a single control channel for controlling a single load unit such as a single HVACR unit. More complex edge node controllers can be provided, for example, by increasing the number of secondary relays.

[0125] As shown in FIG. 4, a signal generated by a thermostat 118 or a building automation system (BAS) controller is input to the circuit, for example, a 24V AC signal or 0VAC. The signal can correspond to a temperature setpoint. The signal is input into a primary relay 120. Primary relay 120 is configured to process an Automated Demand Reduction (ADR) and to take into consideration signals from an Al command center, for example, to receive real-time weather data, forecast weather data, price-signal operator data for preheating and pre-cooling, a grid operator-commanded Demand Response, data or estimates from a Virtual Power Plant,and the like. The signal can be modified at primary relay 120 and passed to or through a secondary relay 122. The resulting signal, or pass-through signal, output from secondary relay 122 is sent a control terminal of a load unit, for example, an HVACR device, BAS, solar generator, other power-generator, or the like.

[0126] Secondary relay 122 can be configured to provide a Permanent Demand Reduction (PDR), for example, through administration of an Al command center control signal. The Al command center control signal can be sent to an AI / ML- level edge appliance or device such as a compressor, burner, refrigerator, solar power generator, or the like. Through use of the Al command center control signal, a system further comprising an AI / ML-level edge device appliance can be configured to enable intra-cycle optimization and a Permanent Demand Reduction.

[0127] Different operations can be carried out by changing the respective states and combinations of operations of the primary relay and the secondary relay, for example, relays 30 and 50, respectively, shown in FIG. 1C, and relays 120 and 122, shown in FIG. 4.

[0128] Primary relay 120 can be a two-position relay. Primary relay 120 can be a smart control input that can be used to optimize autonomously between Permanent Demand Reduction (PDR) and Automated Demand Reduction (ADR), with input from a user-controlled “slider” selector to choose whether to have more assurance of PDR or more opportunity (in commanded kW load shed, for additional revenues) for ADR.

[0129] A table showing operations that can be enabled by the system of the present invention is shown in FIG. 5. FIG. 5 exemplifies various operations for a single control channel for controlling an HVACR or other appliance.

[0130] In FIG. 5, Items 1 through 5 are operations that can be carried out by using the system of the present invention as an edge node controller. The system of the present invention can be added to and / or incorporated into existing systems and configured to intercept existing control signal circuitry and enable operations for comfort, reduced energy consumption, and automatic optimization of space conditioning. Furthermore, the system of the present invention can be added to and / or incorporated into existing systems and configured to automatically optimize system energy consumption and generation for systems including power-generating edge node appliances.

[0131] The electronic controllers of the present invention and system can be used in multichannel systems. Exemplary “Control Channel” combinations for typical HVACR equipment can include combinations for small, large, and commercial level systems. Exemplary channel assignments for a small-scale system, including a residential system comprising a heat pump,can be Channel 1: cooling signal, Channel 2: heating signal, and Channel 3: Blower / fan signal. For a larger heat pump or gas / electric commercial rooftop unit, exemplary channel assignments for signals can be Channel 1 : Stage 1 cooling, Channel 2: Stage 2 cooling, Channel 3: Stage 1 heating, Channel 4: Stage 2 heating, and Channel 5: Blower / Fan. For even larger, commercial systems such as for a chiller system, a rack refrigeration system, or a large A / C unit, the settings can be Channel 1: Stage 1 cooling, Channel 2: Stage 2 cooling, Channel 3: Stage 3 cooling, Channel 4: Stage 4 cooling, and Channel 5: Chilled water pump or Blower / Fan.

[0132] In an example, a commercially available PACE AI4 unit that is installed in connection with a commercial gas heating and electric cooling rooftop package can be installed with features including current sensing and air duct temperature sensing.

[0133] FIG. 6 is a schematic representation of an exemplary embodiment of an Al-enabled thermostatic device 80 for a 4-channel HVAC unit that includes 2-stage cooling and 2-stage heating. Thermostatic device 80 can be configured to operate larger heat pump units, larger air conditioning units, and larger gas-fired or electric heating units. Thermostatic device 80 can include a plurality of input terminals that can be connected to a respective plurality of sources and signals. Input terminal 82 can be configured to receive a bypass / online command. Input terminal 84 can be configured to receive a signal designating an application type, for example, for controlling a cooling unit, for controlling a heating unit, for controlling a heat pump unit, or the like. Input terminal 86 can be configured to receive a command for either heating, cooling, or automatic control. Software parameters for each of these three inputs can update on event.

[0134] Thermostatic device 80 also includes input terminals 90, 92, 94, 96, 98, 100, 102, 104, and 106. Input terminal 90 can be configured to receive a setpoint temperature signal. Input terminal 92 can be configured to receive a zone temperature signal. Input terminal 94 can be configured to receive a date / time signal. Input terminal 96 can be configured to receive a cooling stage 1 command. Input terminal 98 can be configured to receive a cooling stage 2 command. Input terminal 100 can be configured to receive a heating stage 1 command. Input terminal 102 can be configured to receive a heating stage 2 command. Input terminal 104 can be configured to receive a reversing valve command. Input terminal 106 can be configured to receive a reset signal. Each of terminals 90, 92, 94, 96, 98, 100, 102, 104, and 106 can be configured to receive software update signals, for example, at an update rate of one update per second.

[0135] Thermostatic device 80 also includes output terminals 110, 112, 114, and 116. Output terminal 110 can be configured to send to the two-stage cooling unit a Y1 cooling stage1 command. Output terminal 112 can be configured to send to the two-stage cooling unit a Y2 cooling stage 2 command. Output terminal 114 can be configured to send to the two-stage heating unit a W1 heating stage 1 command. Output terminal 116 can be configured to send to the two-stage heating unit a W2 heating stage 2 command. Each of terminals 110, 112, 114, and 116 can be configured to send updated command signals, for example, at an update rate of one update per second.

[0136] Whether the Y1 cooling stage 1 command that is sent to the two-stage cooling unit from terminal 110 is the same as the cooling stage 1 command received at input terminal 96 depends on the processing of signals with the electronic controller using a primary relay and a secondary relay as described above and shown with reference to FIG. 1C. Whether the Y2 cooling stage 2 command that is sent to the two-stage cooling unit from terminal 112 is the same as the cooling stage 2 command received at input terminal 98 depends on the processing of signals with the electronic controller using a primary relay and a secondary relay as described above and shown with reference to FIG. 1C. Whether the W1 heating stage 1 command that is sent to the two-stage heating unit from terminal 114 is the same as the heating stage 1 command received at input terminal 100 depends on the processing of signals with the electronic controller using a primary relay and a secondary relay as described above and shown with reference to FIG. 1C. Whether the W2 heating stage 2 command that is sent to the two- stage heating unit from terminal 116 is the same as the heating stage 2 command received at input terminal 102 depends on the processing of signals with the electronic controller using a primary relay and a secondary relay as described above and shown with reference to FIG. 1C.

[0137] FIG. 7 is a flowchart depicting a method 121 according to an embodiment of the present invention, wherein the method includes a step 123 of receiving conditioning data for an energy consuming and energy generating system. Method steps 125, 126, 128, 130, 132, and 134 involve processing the conditioning data using Al software to create an optimized control signal and estimating a baseline energy consumption and supply of the system. Energy can be generated from, for example, a solar generator appliance that operates as just one component of an energy consumption and generation system. Energy generated that is in excess of the amount needed to run the energy-consuming appliances of the system can be directed back to a power grid or to a battery charging facility.

[0138] FIG. 8 is a flowchart depicting a method 136 according to an embodiment of the present invention, wherein the method includes a step 138 of receiving conditioning data for a refrigeration system. Steps 140, 142, 144, 146, 148, and 150 involve processing the conditioning data using Al software to create an optimized thermostatic control signal and estimating a baselineenergy consumption of the refrigeration system. The refrigeration system can include a refrigerant line temperature sensor and a current sensor that can send signals to the Al command center to be used in generating one or command signals, for example, a temperature modification signal for changing the temperature of a refrigerated space.

[0139] FIG. 9 is a flowchart depicting a method 152 according to an embodiment of the present invention, wherein the method includes a step 154 of receiving conditioning data for an entire HVACR system. Steps 156, 158, 160, 162, 164, and 166 involve processing the conditioning data using Al software to create an optimized thermostatic control signal and estimating a baseline energy consumption and / or demand reduction of the entire system.

[0140] Functions of the controller and other features of the process control logic and calculation schemes that can be used in methods of the present invention, for energy control and savings estimates, are illustrated in FIGS. 10-20, and can be implemented using software that is executable with the indicated microprocessor of an electronic controller of the present invention. Examples of controllers for automatic control of duty cycles HVACR equipment and systems, which can be used in connection with the present invention, and methods using the same, are described in U.S. Patent No. US 10,151,506 B2 to Kolk, and U.S. Patent No. US 10,782,032 B2 to Kolk, each of which is incorporated herein in its entirety be reference.

[0141] FIG. 10 shows process control logic of an energy control algorithm, which is identified by numeral 300, which can be applied by an electronic controller of the present invention to provide automatic energy control and energy savings estimations for an HVACR system. In FIG. 10, the uOEM and uPACE control signals are produced using the estimated plant model description for a hysteresis thermostat only. A block diagram is presented in FIG. 11 of a general hysteresis thermostat, which is identified by numeral 400, which can be used for either heating or cooling. In FIG. 11 (and FIGS. 12 and 13), the “Merge” block represents if-then-else logic with regard to the b, t, and f inputs and x output. In FIG. 10, the uPACE control signals are produced by the “Delay” block. This block applies a time delay (second input on left) to the input signal (first input on left) using the logic presented in FIG. 12. The process logic in FIG. 12 is identified by numeral 500. In the “Delay” block shown in FIG. 12, the “u” signal is the input signal and the “upv” signal is a delayed value of the “u” signal. When the difference between “u” - “upv” is positive, the input signal has transitioned from its 0 to 1 state (OFF to ON). When this occurs, the feedback loop calculates an elapsed time and when this elapsed time > “timeDelay”, the output signal “uTimeDelayed” turns ON. To prevent the timer from delaying when the input signal transitions from 1 to 0 (ON to OFF), the “uTimeDelayed” signal is multiplied by the “upv” signal.

[0142] In FIG. 10, the block titled “Calc Energy” calculates the signal energy for either the uOEM or uPACE signals. A block diagram of this block is presented in FIG. 13, wherein the process logic of this block is identified by numeral 600. The “tWindow” value is globally set as the moving window width, for illustration purposes, this was set to 2000 seconds, however, it can be set to any value, obviously, larger values will require more time to calculate. A single energy calculation is made then every tWindow seconds. The S&H block in FIG. 13 samples and holds the calculated energy every tWindow seconds.

[0143] In FIG. 10, the “Energy Saving Fraction Actual, E” signal is the energy difference of the uOEM signal minus the uPACE signal normalized to the uOEM signal energy. In situations where the uPACE signal energy is less than the uOEM signal energy, the difference will be positive and will lie between 0 and 1. The “Energy Saving Fraction Reference, E*” signal represents a desired energy saving that is sought to be achieved. The “Energy Saving Control Law” is an integral controller operating on the error signal created by subtracting the “Energy Saving Fraction Actual, E” signal from the “Energy Saving Fraction Reference, E*” signal. The integral control law will reduce the error signal to zero by adjusting the time delay signal, “td”, whatever value is required for the uPACE controlled model to achieve the “Energy Saving Fraction Reference, E*”. The desired or selected value of Energy Saving Fraction Reference, E*, can be inputted to the controller by a user, such as via the on-board user input interface or remote user input device indicated in the discussion of FIG.1A.

[0144] FIG. 14 shows process control logic of the “Plant Model” module shown in FIG. 10, which models the dynamics of a forced air heating, cooling, or refrigeration system under hysteresis (OEM) thermostat control. This process control logic, which is identified by numeral 700 in FIG. 14, is applied by the indicated electronic controller according to an example of the present invention. The following terms shown in FIG. 14 are defined as follows: tO AT = outside air temperature; tSetPoint = thermostat setpoint temperature; ul = thermostat control signal to compressor or burner(ON or OFF); u2 = thermostat control signal to blower (ON or OFF); Supply Air Temperature = Conditioned space register entry temperature; Airflow = Conditioned space register entry airflow (equipment nameplate value); tZone = zone (conditioned space) temperature; A = Thermal resistivity between the conditioned space and outside air (includes the zone thermal capacitance); and B = Heat transfer coefficient (includes the zone thermal capacitance).

[0145] A and B are the two parameters that describe the operation of a forced air on / off heating, cooling, and refrigeration equipment under closed loop temperature and / or humidity control via hysteresis thermostat. These parameters vary over time based on load, ambient,and setpoint changes, however, if they are known, energy and power consumptions can be accurately calculated. Two methods for estimating A and B are illustrated herein, which are referred to herein as “Method 1” and “Method 2.”

[0146] In both methods, the “Supply Air Temperature” can be assumed to be 15 degrees less than the zone temperature for cooling applications. For heating applications, the “Supply Air Temperature” can be assumed to be 15 degrees greater than the zone setpoint temperature. This estimate is not critical to the parameter estimation process. When the FIG. 10 system is under on / off thermostat control, both the blower and compressor (or heating coil) are controlled in either of two states; ON or OFF. In the OFF state, the B value becomes 0 because the airflow has been turned OFF by the thermostat.

[0147] Method 1: Method 1 estimates the two model parameters, A and B, using two sensed signals, the Outside Air Temperature and the On / Off status of the equipment. The following information is provided: type of application: heating, cooling, or refrigeration. The following assumptions are made: (i) the thermostat deadband is + / - 1 °F, the setpoint for cooling is 72 °F, for heating is 68 °F, and for refrigeration is 40 °F (these values can be changed but they are assumed to not be directly measurable). During the OFF state, the plant model is presented in FIG. 15, and which is identified therein by numeral 800. Using the assumptions, given information, and the sensor information, the A parameter can be calculated during the OFF state using a calculation scheme shown in FIG. 16 and identified by numeral 900. If only the outside air temperature and OFF time values are known, reasonable values can be selected for the zone temperature and deadband (based on the application type: cooling, heating, or refrigeration) and the A parameter can be estimated. The tZone(k) value is set to tZone(O) = tSetPoint - db / 2. Similarly, considering the same cooling application in the ON state, the block diagram for the ON operation is presented in FIG. 17 and identified by numeral 1000. Using the assumptions, given information, and the sensor information, and the previously computed A parameter (which remains fixed during this calculation), the B parameter can be calculated as shown by the calculation scheme in FIG. 18 and identified by numeral 1100. If thermostat deadband, zone temperature, outside air temperature, and ON time values are known, the B parameter can be calculated exactly. If only the outside air temperature and ON time values are known, reasonable values can be selected for the zone temperature and deadband (based on the application type: cooling, heating, or refrigeration) and the B parameter can be estimated. As data is collected from the outside air temperature, the ON time, the OFF time, and (optionally) the zone temperature and the thermostat deadband, the A and B parameters can be dynamically refined.

[0148] Method 2: For this method, there are three available sensor signals for the estimation of the A and B parameters, which are the Outside Air Temperature, the On / Off Status of the Equipment, and the zone temperature. The following information is provided for this method: type of application: heating, cooling, or refrigeration. This approach uses an Extended Kalman Filter (EKF) to estimate the A and B parameters. However, any recursive estimation approach could be used (e.g., EKF, least squares, neural networks, fuzzy logic, observer, or other estimation methods). The differential, difference, state, and partial equations which can be used to implement the EKF are shown in FIG. 19 and identified by numeral 1200. A computer software implementation of the EKF algorithm, using the equations shown in FIG. 19, can be used to estimate the A and B parameters for this method. Initially assumed A and B parameter values used in the first iteration of the calculations can be arbitrary preselected values.

[0149] An illustration of the plant model with EKF according to indicated Method 2 is shown in FIG. 20 and is identified by numeral 1300. As described in the examples included herein, the performance of the EKF can be validated using measurement signal data from this Plant model simulation. The EKF receives three measurement signals from the plant; tOAT, compressor and blower commands, and tZone and estimates the A and B parameters. The true A and B parameter values are embedded in the Plant model. As shown in the examples included herein, the EKF performance is able to rapidly and accurately estimate the values of the A and B parameters.

[0150] As indicated by the discussions of FIGS. 10-20, this present invention (1) can achieve energy savings by applying a calculated dynamically varying time delay to the OEM control signal (“uOEM”) to produce an adjusted control (“uPACE”), which is applied to the equipment to be controlled, and (2) estimates the energy saved using the adjusted control in place of the OEM control. As indicated, an overall configuration of the energy controller is presented in FIG. 17. Since the equipment can be controlled by the adjusted control signal (“uPACE” control signal), the indicated plant model can be used to estimate what the thermostat control signal (“uOEM” control signal) would have looked like had the plant been controlled by it. As indicated, this is achieved by sensing the zone temperature and the outside air temperature, and the energy present in the uOEM signal is then calculated over a moving time window. As also indicated, an identical plant model but including a time delay on the control signal is then used to estimate the uPACE control signal, and the energy present in the uPACE signal can be calculated over the same moving time window. The energy difference, normalized to the uOEM energy, is calculated and used as a feedback signal in the indicatedintegral control algorithm whose output is the time delay value used to create the uPACE signal. The feedback signal is controlled at a normalized energy savings setpoint (which can be set to any value between 0 and 1).

[0151] The wiring terminations for an example of an installation configuration of an electronic controller of the present invention is presented with reference made to FIG. 21. FIG. 21 shows an electrical connection diagram 1400 for a single stage cooling application using an electronic controller according to an example of the present invention. This configuration can be used when a single air conditioner thermostat is used to control one HVAC cooling device (a compressor). This configuration also supports thermostats that provide a manual switch to select either heating or cooling operation. The compressor can be a compressor suitable for use in vapor-compression cooling / refrigeration systems. The compressor can include an electric motor (not shown), used to drive the compressor. The electric motor itself can be a conventional electric motor or other suitable electric motor used or useful for driving such load units.

[0152] In the example shown in FIG. 21, electronic controller 1018 provides two independent control channels that may be wired to support different equipment configurations. Referring to the first pin module 1001, the first channel 1001 A comprises one of pins 1-4, and the second channel 1001B comprises one of pins 5-6 thereof. Output lines to the load unit(s), e.g., a cooling unit compressor, are shown as extending from one of pins 1-3. Pin 4 can be used for hardwire input of temperature signals transmitted from a temperature sensor 22 located inside or outside of the building in which a space is being temperature conditioned. As indicated, temperature sensor 22 alternatively can communicate with the controller 1018 via a wireless connection therewith, and / or can be integrated with the electronic controller, for example, outside if the controller is also located outside (not shown). In addition, the controller provides a separate “dry contact” input channel that may be used for remote control of the controller, such as by an existing BMS system. Referring to the second pin module 1010, pins 1-2 thereof can be used for this dry contact input module. A communication port 1020 is shown in these figures as a mini-USB port (e.g., a camera size USB port) but is not limited thereto. A service tool, computer, smartphone or other device (not shown) can be used to import / input parameters, and the like into electronic controller 1018 by making a communication link with the controller via port 1020. Electronic controller 1018 can have the indicated controller programs preloaded into the controller on-board memory during its assembly and before installation in the field.

[0153] The thermostat (e.g., an OEM thermostat) which can be used with the electronic controller of the present invention, such one having the wiring configuration shown in FIG. 21 or another configuration, can deployed at some point in a building and senses the temperature of the ambient air and if it is higher than the comfort setting which has been selected, sends a signal to activate the air conditioning unit. As indicated, in the present invention, the electronic controller intercepts the thermostat signal, which powers up the electronic controller to process the signal according to the programmed algorithm of the controller before sending a controller- processed output signal to the load unit. The air conditioning unit typically comprises the compressor, and a condenser and evaporator connecting with each other in a closed refrigerant system (not shown). The refrigeration cycle itself is well known (e.g., see, U.S. Patent No. 4,094,166, which is incorporated herein by reference in its entirety). Basically, gaseous refrigerant is delivered from the compressor to the condenser coil where it gives up heat and then is passed through an expansion valve to the evaporator coil where it absorbs heat from the circulating air which is passed thereover by the evaporator fan. When the thermostat senses that the ambient air has been cooled to the selected level, the thermostat goes to an off state to turn off the compressor, evaporator fan and condenser fan until the ambient temperature has again reached the level where further cooling is necessary. As indicated, the electronic controller of the present invention goes to sleep when the thermostat stops signaling the load unit, until the next power on signal is sent by the thermostat to the same load unit which, as indicated, will be intercepted by the electronic controller which powers up the electronic controller to process the signal according to its programmed algorithm before sending a controller-processed output signal to the load unit. As indicated, a deadband typically is applied to the control temperature setting at the thermostat, which deadband effectively can be modified by the electronic controller to improve demand savings in a controlled manner.

[0154] The indicated pin assignments for the first channel 1001 A and second channel 100 IB that are identified in FIG. 21 can apply in similar pin module for other types of load units of an HVACR system, such as a dual stage cooling unit, a heating unit (e.g., gas, electric, heat pump), a boiler, and so forth. Other aspects of an electrical connection configuration that can be used in these other types of load units can be readily adapted and implemented as applicable. In these manners, for example, an electronic controller having the indicated demand regulator controller is operable to intercept and process a thermostat’ s control signal with an algorithm that can automatically generate enhanced control signals to provide energy and savings control. Amongst other benefits and advantages, existing HVACR systems, forexample, can embody the present controller such as illustrated herein to improve energy consumption and reduce energy costs of heating, cooling, and refrigeration equipment.

[0155] The edge device according to the present invention can be used in a method of the present invention wherein, via optimization rules, an HVACR unit can be operated to not run in a vapor compression air conditioning mode or in a heating mode if the sensed return air or fluid temperature is within a specific range, for example, (>) (<) X. In this way, the edge device can be used to accomplish the aims of HVAC unit economizer operation, without the expense of an economizer. Greater details about such use and optimization can be found in U.S. Patent Application Publication No. US 2016 / 0025364 Al to Mills, Jr. et al., which is incorporated herein in its entirety by reference.

[0156] The edge device can be configured to provide a user-controllable “slider’- that can be used to apply a customizable set of variables for building operators or industrial process control operators. The slider can provide, at one end, more energy savings and / or demand reduction, and at the other end, more operation that is characteristic of native control architecture operation. The adjustable slider can thus provide more assurance to an operator of an “as normal” comfort while at the same time assuring that performance variables are achieved. Greater details about such a configuration and use can be found in U.S. Patent Application Publication No. US 2016 / 0025364 Al to Mills, Jr. et al.

[0157] For the edge device, the Al control algorithms can calculate optimized starts per hour for HVACR and other equipment, for example, by using library databases of OEM recommendations and sensed data. The Al control algorithms can provide limits on equipment starts to ensure compliance with equipment manufacturer specifications and can enforce antishort-cycle and other machine protections. The Al control algorithms and sensed data can also use the abovementioned variables to detect and correct anomalies in HVACR and other equipment operation, in addition to the equipment operation examples set forth herein.

[0158] The edge device can utilize Al control algorithms that utilize a multiplicity of weather, electric price, operational, and other inputs to conduct optimized pre-cooling or preheating of a conditioned space.

[0159] The edge device can utilize Al control algorithms that improve performance of HVACR or other equipment that is oversized, or undersized, for the cooling, heating, or other load to which it is connected. Such an improvement can be particularly beneficial in situations wherein the native control architecture is delivering sub-optimal energy efficiency.

[0160] The edge device can utilize Al control algorithms that can be used in combination with a photovoltaic (PV) solar array to be able to dynamically reduce HVAC building energyusage, in order to maintain a higher level of reliability for dispatchable net generation to the power grid, from the PV solar array.

[0161] The edge device can be used with machines that utilize natural gas or other flammable substances. The Al control algorithms can incorporate an intelligent emergency shutoff at the machine unit.

[0162] The edge device can be configured to utilize Al control algorithms that are applied to monitor and manage refrigerant systems in HVACR equipment. For example, the Al control algorithms can be applied to monitor for refrigerant leakage, utilizing, for example, supply and return refrigerant temperatures, refrigerant pressures, and other data.

[0163] The Al control algorithms together with sensors can provide easily added monitoring and control of building indoor air quality (IAQ) and can also be integrated with duct-level or elsewhere-placed ultraviolet disinfection devices.

[0164] The present invention will be further clarified by the following examples, which are intended to be exemplary of the present invention.EXAMPLESExample 1

[0165] Performance of the plant model with Extended Kalman Method (EKF) in indicated Method 2 to estimate the model parameters A and B, was evaluated as follows. The EKF used in this method was tested using measurement signal data from the Plant model simulation shown in FIG. 20. A computer software program which embodied the equations shown in FIG. 19 was used for this simulation. The EKF received three measurement signals from the plant in the simulation, which were tOAT, compressor and blower commands, and tZone, and estimated the A and B parameters. The true A and B parameter values are embedded in the Plant model. Two tests are conducted, one using constant values for the true A and B parameters, the other using a constant A and varying B parameters to model heating load variations in the zone. The EKF performance was judged on its ability to rapidly and accurately estimate the values of these two parameters.

[0166] In Test 1: The simulation model was configured with A and B set to constant values; A = le-4 (i.e., 0.0001) and B = 6e-3 (i.e., 0.006). Plots of the EKF estimated values and actual values of parameter A are presented in the plots shown in FIG. 15A and FIG. 15B of U.S. Patent No. US 10,151,506 B2 that is incorporated herein in its entirety be reference. Plots of the EKF estimated values and actual values of parameter B are presented in the plots shown in FIG. 16A and FIG. 16B of U.S. Patent No. US 10,151,506 B2.

[0167] In Test 2: The simulation model was configured with a constant parameter A and a time varying B parameter. Specifically, the simulation model was configured with A set to a constant value of parameter A = le-4 (i.e., 0.0001) and B set to a time varying value to model the effects of dynamically changing loads in the conditioned air space. Plots of the EKF estimated values and actual values of parameter A are presented in the plots shown in FIG. 17A and FIG. 17B of U.S. Patent No. US 10,151,506 B2. Plots of the EKF estimated values and actual values of parameter B are presented in the plot shown in FIG. 18 of U.S. Patent No. US 10, 151,506 B2.

[0168] As shown in the test results for Tests 1 and 2, the EKF quickly and accurately estimates the values of the two parameters, A and B.Example 2

[0169] A simulation of an operation of a single stage cooling system, wherein a single thermostat is used to control one compressor, such as shown in FIG. 21, was performed on a computer model that was adapted to simulate operation of the electronic controller that applies the process control logic shown in FIGS. 10-15, 17 and 20 and calculation schemes of FIGS. 16, 18, and 19 herein. The developed model was based in part on actual data obtained from operation of the same equipment in the indicated single stage cooling configuration and with the OEM thermostat alone in the field. The simulation model is calibrated to agree with field data.

[0170] Simulation results displaying the performance of the energy control algorithm are presented in the following time history plots. In FIG. 19, of U.S. Patent No. US 10,151,506 B2, an “Energy Saving Fraction Reference, E*” profile was applied to the controller. The actual energy saved using the integral controller to adjust the time delay is shown. Plots of the values of these parameters are identified in FIG. 19 of U.S. Patent No. US 10, 151,506 B2. The profile begins at .1 (10% energy savings), at time = 150,000 seconds, the E* profile increases to .2 (20% energy savings). Between 300,000 and 450,000 seconds the E* decreases back to .1 and from 450,000 seconds onward, it increases to .15 (15% energy savings). The 9000 second low pass filter in FIG. 19 of U.S. Patent No. US 10,151,506 B2 is used to smooth the E signal for presentation in a time history plot. It is not used in the control algorithm. The time history plot shown in FIG. 20 of U.S. Patent No. US 10,151,506 B2 presents the time delay signal calculated by the integral controller. This is the value used by the “time delay” block in FIG. 17 to create the uPACE control signal. The time history plots shown in FIG. 21 of U.S. Patent No. US 10,151,506 B2 present the regulated zone temperature signals for the uOEM and uPACE controlled spaces. As shown by the plots, the temperature variation increases as energy is saved.

[0171] The present invention includes the following aspects / embodiments / features in any order and / or in any combination:1. The present invention relates to a method of reducing energy consumption and / or demand of an edge device appliance, comprising: receiving conditioning data pertaining to a thermostatic output of the edge device appliance in a conditioned space, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs; processing the conditioning data using artificial intelligence (Al) software to create an optimized thermostatic control signal for the edge device appliance in the conditioned space; controlling the edge device appliance in the conditioned space based on the optimized thermostatic control signal; receiving an energy consumption modification command, the energy consumption modification command including or activating a retrieval of modification data, the modification data comprising a demand reduction command value, a time-of-day energy rate, weather information, or a combination thereof; receiving power consumption data from sensors to determine real-time energy consumption data of the edge device appliance in the conditioned space; generating an energy consumption modification signal using the Al software, the energy consumption modification signal being based on the conditioning data, the modification data, and the real-time energy consumption data; and controlling the edge device appliance in the conditioned space based on the energy consumption modification signal, wherein the energy consumption modification signal comprises a modification of timing of ON / OFF commands for the appliances to achieve energy consumption reduction.2. The method of any preceding or following embodiment / feature / aspect, further comprising sending, from the edge device appliance, sensor outputs for updating the optimized thermostatic control signal.3. The method of any preceding or following embodiment / feature / aspect, wherein the sensor outputs for updating the optimized thermostatic control signal are continuously sent from the edge device appliance.4. The method of any preceding or following embodiment / feature / aspect, further comprising estimating baseline energy consumption data of the edge device appliance in the conditioned space based on the conditioning data, and the energy consumption modification signal is further based on the estimated baseline energy consumption data.5. The present invention relates to a method for automatically controlling and managing energy consumption and operation of at least one edge appliance in an HVACR system, the at least one edge appliance comprising a hysteresis thermostat and being configured to operate based on a control signal, the method comprising the steps of: receiving a thermostat command signal from the hysteresis thermostat; receiving a first Al command signal at a primary control relay of a controller, the controller comprising the primary control relay and a secondary control relay that is arranged in series with the primary control relay, the Al command signal being generated by an artificial intelligence (Al) command center that is remote from the controller, the Al command center comprising a processor that runs one or more Al control algorithms to generate the Al command signal, the primary control relay having an open state and a closed state, and the secondary control relay having an open state and a closed state, wherein when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the secondary control relay, and the output of the primary control relay is the Al command signal, when the state of the primary control relay is closed, the output of the primary control relay is the thermostat command signal, when the state of the secondary control relay is closed, the output of the primary control relay is sent to the Al command center, processed by the one or more Al control algorithms to form a processed signal, and the processed signal is passed to the at least one edge appliance as the control signal, and when the state of the secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay; and based on the states of the primary control and the secondary control relay, sending the control signal to the at least one edge appliance.6. The method of any preceding or following embodiment / feature / aspect, further comprising: sensing an outside temperature proximal to the at least one edge appliance, to form a sensed outside temperature; generating a temperature signal based on the sensed outside temperature; sending the temperature signal to the controller; and either (i) sending the temperature signal from the controller to the Al command center to be processed by the one or more Al control algorithms and taken into account when formingthe processed signal, or (ii) adjusting the control signal at the controller to form an adjusted control signal and then sending the adjusted control signal to the at least one edge appliance.7. The method of any preceding or following embodiment / feature / aspect, further comprising: sensing an ambient temperature in a space to be heated or cooled by the at least one edge appliance, to form a sensed ambient temperature; generating a temperature signal based on the sensed ambient temperature; sending the temperature signal to the controller; and either (i) sending the temperature signal from the controller to the Al command center to be processed by the one or more Al control algorithms and taken into account when forming the processed signal, or (ii) adjusting the control signal at the controller to form an adjusted control signal and then sending the adjusted control signal from the controller to the at least one edge appliance.8. The method of any preceding or following embodiment / feature / aspect, further comprising: inputting forecast temperature information for a location outside, but proximal to, a space to be heated or cooled by the edge appliance, the inputting comprising inputting the forecast temperature information into the one or more Al control algorithms; and adjusting the control signal at the Al command center, to form an adjusted control signal, based on the input forecast temperature information.9. The method of any preceding or following embodiment / feature / aspect, further comprising: inputting sensed current temperature information for a location outside, but proximal to, a space to be heated or cooled by the edge appliance, the inputting comprising inputting the sensed current temperature information into the one or more Al control algorithms; and adjusting the control signal at the Al command center, to form an adjusted control signal, based on the input sensed current temperature information.10. The method of any preceding or following embodiment / feature / aspect, wherein the controller further comprises a display, and the method further comprises: sending information display signals from the Al command center to the controller; and displaying information on the display, resulting from the information display signals.11. The method of any preceding or following embodiment / feature / aspect, further comprising: sending an updated software version from the Al command center to the controller; and updating software in the controller based on the updated software version.12. The method of any preceding or following embodiment / feature / aspect, further comprising: sending update signals based on the updated software version, from the controller to the at least one edge appliance; andupdating software in the at least one edge appliance, based on the update signals.13. The method of any preceding or following embodiment / feature / aspect, wherein energy to power the at least one edge appliance comes from a power grid, and the method further comprises: receiving, at the Al command center, information pertaining to usage of energy by the power grid; and processing the information pertaining to usage of energy by the power grid, with the Al control algorithms, and forming the processed signal based on the information pertaining to usage of energy by the power grid.14. The method of any preceding or following embodiment / feature / aspect, wherein energy to power the at least one edge appliance comes from a power grid, and the method further comprises: receiving, at the Al command center, information pertaining to (i) time of day energy rate, and (ii) demand response; and processing the information pertaining to (i) time of day energy rate, and (ii) demand response, with the Al control algorithms, and forming the processed signal based on the information pertaining to (i) time of day energy rate, and (ii) demand response.15. The method of any preceding or following embodiment / feature / aspect, further comprising: estimating, with the Al control algorithms, the thermal capacitance and thermal resistance of the space to be heated or cooled by the at least one edge appliance; estimating, with the Al control algorithms, the power consumption needed to provide a comfortable ambiance in the space to be heated or cooled; estimating power and energy savings that would be achieved by a hypothetical control signal that would provide the comfortable ambiance; generating a particular control signal with the Al control algorithms, on the basis of the hypothetical control signal; and sending the particular control signal from the Al command center to the controller.16. The method of any preceding or following embodiment / feature / aspect, further comprising: intercepting, at the controller, an original equipment manufacturer (OEM) thermostat command signal in-route from the hysteresis thermostat to the at least one edge appliance; and instead of sending the OEM thermostat command signal to the at least one edge appliance, sending the control signal to the at least one edge appliance.17. The method of any preceding or following embodiment / feature / aspect, further comprising:modifying the control signal by modulating the open and closed states of the secondary control relay, wherein the modulating causes the at least one edge appliance to cycle on and off, and the cycling is timed to take advantage of energy stored in a thermal capacitance of the space to be heated or cooled by the at least one edge appliance.18. The present invention also relates to an edge node device for controlling at least one edge appliance in an HVACR system, the edge node device configured for sending a control signal to the at least one edge appliance, the at least one edge appliance having a thermostat, the edge node device comprising: a controller, the controller comprising a primary control relay and a first secondary control relay that is arranged in series with the primary control relay, the primary control relay comprising a first artificial intelligence (Al) command input, the first secondary control relay comprising a second Al command input, both the first Al command input and the second Al command input being configured to be in communication with an Al command center for receiving a first Al command signal and a second Al command signal, respectively, the primary control relay having an open state and a closed state, and the first secondary control relay having an open state and a closed state; and an output configured to be in communication with the at least one edge appliance and configured to send a control signal from the controller to the at least one edge appliance, wherein the edge node device is configured such that when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the first secondary control relay, and the output of the primary control relay is the Al command signal, when the state of the primary control relay is closed, the output of the primary control relay is a thermostat command signal generated by the thermostat, when the state of the first secondary control relay is closed, the output of the primary control relay is sent to the Al command center to be processed by one or more Al control algorithms and form a processed signal that is passed to the at least one edge appliance as the control signal, and when the state of the first secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay.19. The present invention also relates to a network comprising the edge node device of any preceding or following embodiment / feature / aspect, and an Al command center, wherein the Al command center is configured to generate the first Al command signal.20. The network of any preceding or following embodiment / feature / aspect, wherein the Al command center is remote from the edge node device.21. The network of any preceding or following embodiment / feature / aspect, wherein the Al command center is a cloud-based command center.22. The network of any preceding or following embodiment / feature / aspect, wherein the Al command center is remote from the edge node device and communicates with the edge node device via cloud-based computing.23. The present invention also relates to an edge node device of any preceding or following embodiment / feature / aspect, wherein: the edge node device is configured for sending a plurality of control signals to a respective plurality of edge appliances each of has a thermostat; the controller comprising a primary control relay a plurality of secondary control relays including the first secondary relay; each of the secondary control relays is respectively arranged in series with the primary control relay; each of the secondary control relays comprising a respective second Al command input, each of the second Al command inputs being configured to be in communication with the Al command center for receiving a respective second Al command signal; each of the secondary control relays has an open state and a closed state; the controller has a plurality of respective outputs each configured to be in communication with a respective one of the plurality of edge appliances and configured to send a respective control signal from the controller to the respective edge appliance, and the edge node device is configured such that, for each respective edge appliance of the plurality of edge appliances, when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the respective secondary control relay, and the output of the primary control relay is the Al command signal, when the state of the primary control relay is closed, the output of the primary control relay is a thermostat command signal generated by the thermostat, when the state of the respective secondary control relay is closed, the output of the primary control relay is sent to the Al command center to be processed by one or more Al control algorithms and form a processed signal that is passed to the respective edge appliance as the control signal, andwhen the state of the respective secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay.24. The present invention also relates to a network comprising an edge node device of any preceding or following embodiment / feature / aspect, and an Al command center, wherein the Al command center is configured to generate the first Al command signal.25. The present invention also relates to a system comprising the edge node device of any preceding or following embodiment / feature / aspect, an HVACR appliance, a current sensor, and an air duct temperature sensor, wherein the current sensor is configured to send a sensed current signal to the controller, the air duct temperature sensor is configured to send a sensed air duct temperature signal to the controller, and the controller is configured to send data pertaining to the sensed current signal and the sensed air duct temperature signal to the Al command center.26. The system of any preceding or following embodiment / feature / aspect, further comprising an Al command center.27. The system of any preceding or following embodiment / feature / aspect, further comprising a cloud-based learning database of curated data, wherein the Al command center is in communication with and configured to retrieve data from the cloud-based learning database of curated data.28. The present invention also relates to a non-transitory computer readable storage medium storing instructions which, when executed by a computer, cause the computer to execute a process, the process comprising: receiving conditioning data pertaining to a thermostatic output of the edge device appliance in a conditioned space, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs; processing the conditioning data using artificial intelligence (Al) software to create an optimized thermostatic control signal for the edge device appliance in the conditioned space; controlling the edge device appliance in the conditioned space based on the optimized thermostatic control signal; receiving an energy consumption modification command, the energy consumption modification command including or activating a retrieval of modification data, the modification data comprising a demand reduction command value, a time-of-day energy rate, weather information, or a combination thereof;receiving power consumption data from sensors to determine real-time energy consumption data of the edge device appliance in the conditioned space; generating an energy consumption modification signal using the Al software, the energy consumption modification signal being based on the conditioning data, the modification data, and the real-time energy consumption data; and controlling the edge device appliance in the conditioned space based on the energy consumption modification signal, wherein the energy consumption modification signal comprises a modification of timing of ON / OFF commands for the appliances to achieve energy consumption reduction.29. The method of any preceding or following embodiment / feature / aspect, further comprising one or more of steps of: (1) applying pre-heating or pre- cooling using time of day energy costs; (2) applying pre-heating or pre-cooling based on forecasted weather information or data; (3) applying a plurality of equipment "on" time values based on a demand response command signal; (4) reducing the number of thermostat "on" calls if a device is estimated to be oversized; (5) modulating thermostat "on" calls to prevent temperature overshoot and undershoot, (6) modulating thermostat "on" calls to reduce "run on" time, and (7) modulating thermostat “off’ calls to increase off times.30. The method of any preceding or following embodiment / feature / aspect, further comprising: receiving conditioning data pertaining to a thermostatic or other control output of the at least one edge device appliance, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs; processing the conditioning data, with the one or more (Al) control algorithms to create an optimized thermostatic control signal for the at least one edge device appliance in a conditioned space; controlling the at least one edge device appliance in the conditioned space based on the optimized thermostatic control signal; adaptively estimating a thermal capacitance of the conditioned space, a thermal resistance of the conditioned space, and an internal load of the conditioned space; executing a plant model based on the estimated values using measured measurement data and an estimated temperature setpoint, wherein the estimated temperature setpoint and a conditioned space temperature are produced by the plant model and applied to a hysteresis temperature controller model to create an estimated thermostat signal; and estimating energy savings based on an integrated difference between the estimated thermostat signal produced by the plant model and a control signal.

[0172] The present invention can include any combination of these various features or embodiments above and / or below as set forth in sentences and / or paragraphs. Any combination of disclosed features herein is considered part of the present invention and no limitation is intended with respect to combinable features.

[0173] The entire contents of all references cited in this disclosure are incorporated herein in their entireties, by reference. Further, when an amount, concentration, or other value or parameter is given as either a range, preferred range, or a list of upper preferable values and lower preferable values, this is to be understood as specifically disclosing all ranges formed from any pair of any upper range limit or preferred value and any lower range limit or preferred value, regardless of whether ranges are separately disclosed. Where a range of numerical values is recited herein, unless otherwise stated, the range is intended to include the endpoints thereof, and all integers and fractions within the range. It is not intended that the scope of the invention be limited to the specific values recited when defining a range.

[0174] Other embodiments of the present invention will be apparent to those skilled in the art from consideration of the present specification and practice of the present invention disclosed herein. It is intended that the present specification and examples be considered as exemplary only with a true scope and spirit of the invention being indicated by the following claims and equivalents thereof.

Claims

WHAT IS CLAIMED IS:

1. A method of reducing energy consumption and / or demand of an edge device appliance, comprising: receiving conditioning data pertaining to a thermostatic or other control output of the edge device appliance for a conditioned space or other load, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs; processing the conditioning data using artificial intelligence (Al) software to create an optimized thermostatic or other control signal for the edge device appliance for the conditioned space or other load; controlling the edge device appliance for the conditioned space or other load, based on the optimized thermostatic or other control signal; receiving one or more modification commands pertaining to energy consumption and / or demand, the one or more modification commands including or activating a retrieval of modification data, the modification data comprising a demand reduction command value, a time-of-day energy rate, weather information, or a combination thereof; receiving power consumption data from sensors to determine real-time energy consumption and / or demand data of the edge device appliance for the conditioned space or other load; generating an energy consumption and / or demand modification signal using the Al software, the energy consumption and / or demand modification signal being based on the conditioning data, the modification data, and the real-time energy consumption and / or demand data; and controlling the edge device appliance for the conditioned space or other load based on the energy consumption and / or demand modification signal, wherein the energy consumption and / or demand modification signal comprises a modification of timing of operating commands for the appliances to achieve energy consumption and / or demand reduction.

2. The method of claim 1, further comprising sending, from the edge device appliance, sensor outputs for updating the optimized thermostatic or other control signal.

3. The method of claim 2, wherein the sensor outputs for updating the optimized thermostatic or other control signal are continuously sent from the edge device appliance.

4. The method of claim 1, further comprising estimating baseline energy consumption and / or demand data of the edge device appliance for the conditioned space or other load based on the conditioning data, and the energy consumption and / or demand modification signal is further based on the estimated baseline energy consumption and / or demand data.

5. A method for automatically controlling and managing energy consumption and / or demand and operation of at least one edge appliance in an HVACR or other system, the at least one edge appliance comprising a hysteresis thermostat or other control and being configured to operate based on a control signal, the method comprising the steps of: receiving an OEM thermostatic or other control command signal from the hysteresis thermostat or other control; receiving a first Al command signal at a primary control relay of a controller, the controller comprising the primary control relay and a secondary control relay that is arranged in series with the primary control relay, the Al command signal being generated by an artificial intelligence (Al) cloud command center that is remote from the controller, the Al cloud command center comprising a processor that runs one or more Al control algorithms to generate the Al command signal, the primary control relay having an open state and a closed state, and the secondary control relay having an open state and a closed state, wherein when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the secondary control relay, and the output of the primary control relay is the Al command signal, when the state of the primary control relay is closed, the output of the primary control relay is the OEM thermostatic or other control command signal, when the state of the secondary control relay is closed, the output of the primary control relay is sent to the Al cloud command center, processed by the one or more Al control algorithms to form a processed signal, and the processed signal is passed to the at least one edge appliance as the control signal, and when the state of the secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay; and based on the states of the primary control and the secondary control relay, sending the control signal to the at least one edge appliance.

6. The method of claim 5, further comprising:sensing a weather-related or other performance variable proximal to the at least one edge appliance, to form a sensed temperature or other performance variable input; generating a temperature or other performance variable input signal based on the sensed temperature or other performance variable input; sending the temperature or other performance variable input signal to the controller; and either (i) sending the temperature or other performance variable input signal from the controller to the Al cloud command center to be processed by the one or more Al control algorithms and taken into account when forming the processed signal, or (ii) adjusting the control signal at the controller to form an adjusted control signal and then sending the adjusted control signal to the at least one edge appliance.

7. The method of claim 5, further comprising: sensing an ambient temperature in a conditioned space to be heated or cooled by the at least one edge appliance, to form a sensed ambient temperature; generating a temperature signal based on the sensed ambient temperature; sending the temperature signal to the controller; and either (i) sending the temperature signal from the controller to the Al cloud command center to be processed by the one or more Al control algorithms and taken into account when forming the processed signal, or (ii) adjusting the control signal at the controller to form an adjusted control signal and then sending the adjusted control signal from the controller to the at least one edge appliance.

8. The method of claim 5, further comprising: inputting weather forecast temperature information for a location outside, but proximal to, a conditioned space to be heated or cooled by the edge appliance, the inputting comprising inputting the weather forecast temperature information into the one or more Al control algorithms; and adjusting the control signal at the Al cloud command center, to form an adjusted control signal, based on the input forecast temperature information.

9. The method of claim 5, further comprising:inputting sensed current temperature information for a location outside, but proximal to, a space to be heated or cooled by the edge appliance, the inputting comprising inputting the sensed current temperature information into the one or more Al control algorithms; and adjusting the control signal at the Al command center, to form an adjusted control signal, based on the input sensed current temperature information.

10. The method of claim 5, wherein the controller further comprises a display, and the method further comprises: sending information display signals from the Al cloud command center to the controller; and displaying information on the display, resulting from the information display signals.

11. The method of claim 5, further comprising: sending an updated software version from the Al cloud command center to the controller; and updating software in the controller based on the updated software version.

12. The method of claim 11, further comprising: sending update signals based on the updated software version, from the controller to the at least one edge appliance; and updating software in the at least one edge appliance, based on the update signals.

13. The method of claim 5, wherein energy to power the at least one edge appliance comes from a power grid, and the method further comprises: receiving, at the Al cloud command center, information pertaining to usage of energy and demand by the power grid; and processing the information pertaining to usage of energy and demand by the power grid, with the Al control algorithms, and forming the Al command signal based on the information pertaining to usage of energy and demand by the power grid.

14. The method of claim 5, wherein energy to power the at least one edge appliance comes from a power grid, and the method further comprises:receiving, at the Al cloud command center, information pertaining to (i) time of day energy prices, and (ii) demand response and load shifting, load shedding, and virtual power plant opportunities with the power grid; and processing the information pertaining to (i) time of day energy prices, and (ii) demand response and load shifting, load shedding, and virtual power plant opportunities with the power grid, with the Al control algorithms, and forming the Al command signal based on the information pertaining to (i) time of day energy prices, and (ii) demand response and load shifting, load shedding, and virtual power plant opportunities with the power grid.

15. The method of claim 5, further comprising: estimating, with the Al control algorithms, the thermal capacitance and thermal resistance of a conditioned space to be heated or cooled by the at least one edge appliance; estimating, with the Al control algorithms, the power consumption needed to provide a comfortable ambiance in the conditioned space to be heated or cooled; estimating power demand reduction and energy savings that would be achieved by a hypothetical control signal that would provide the comfortable ambiance: generating a particular control signal with the Al control algorithms, on the basis of the hypothetical control signal; and sending the particular control signal from the Al command center to the controller.

16. The method of claim 5, further comprising: intercepting, at the controller, an original equipment manufacturer (OEM) thermostatic or other control command signal enroute from the hysteresis thermostat or other control, to the at least one edge appliance; and instead of sending the OEM thermostatic or other control command signal to the at least one edge appliance, sending the Al command signal to the at least one edge appliance.

17. The method of claim 5, further comprising: modifying the control signal by modulating the open and closed states of the secondary control relay, wherein the modulating causes the at least one edge appliance to cycle on and off, and the cycling is timed to take advantage of energy stored in a thermal capacitance of the space to be heated or cooled by the at least one edge appliance.

18. The method of claim 5, further comprising one or more of steps of: (1) applying pre-heating or pre-cooling using time of day energy costs; (2) applying pre-heating or pre-cooling based on forecasted weather information or data; (3) applying a plurality of equipment "on" time values based on a demand response command signal; (4) reducing the number of thermostat "on" calls if a device is estimated to be oversized; (5) modulating thermostat "on" calls to prevent temperature overshoot and undershoot, (6) modulating thermostat "on" calls to reduce "run on" time, and (7) modulating thermostat “off” calls to increase off times. .

19. The method of claim 5, further comprising: receiving conditioning data pertaining to a thermostatic or other control output of the at least one edge device appliance, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs; processing the conditioning data, with the one or more (Al) control algorithms to create an optimized thermostatic control signal for the at least one edge device appliance in a conditioned space; controlling the at least one edge device appliance in the conditioned space based on the optimized thermostatic control signal; adaptively estimating a thermal capacitance of the conditioned space, a thermal resistance of the conditioned space, and an internal load of the conditioned space; executing a plant model based on the estimated values using measured measurement data and an estimated temperature setpoint, wherein the estimated temperature setpoint and a conditioned space temperature are produced by the plant model and applied to a hysteresis temperature controller model to create an estimated thermostat signal; and estimating energy savings based on an integrated difference between the estimated thermostat signal produced by the plant model and a control signal.

20. An edge node device for controlling at least one edge appliance in an HVACR or other system, the edge node device configured for sending a control signal to the at least one edge appliance, the at least one edge appliance having a thermostat or other control, the edge node device comprising: a controller, the controller comprising a primary control relay and a first secondary control relay that is arranged in series with the primary control relay, the primary control relay comprising a first artificial intelligence (Al) command input, the first secondary control relay comprising a second Al command input, both the first Al command input and the second Alcommand input being configured to be in communication with an Al command center for receiving a first Al command signal and a second Al command signal, respectively, the primary control relay having an open state and a closed state, and the first secondary control relay having an open state and a closed state; and an output configured to be in communication with the at least one edge appliance and configured to send a control signal from the controller to the at least one edge appliance, wherein the edge node device is configured such that when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the first secondary control relay, and the output of the primary control relay is the Al command signal, when the state of the primary control relay is closed, the output of the primary control relay is a thermostatic command signal generated by the thermostat, when the state of the first secondary control relay is closed, the output of the primary control relay is sent to the Al command center to be processed by one or more Al control algorithms and form a processed signal that is passed to the at least one edge appliance as the control signal, and when the state of the first secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay.

21. A network comprising the edge node device of claim 20 and an Al command center, wherein the Al command center is configured to generate the first Al command signal.

22. The network of claim 21, wherein the Al command center is remote from the edge node device.

23. The network of claim 21, wherein the Al command center is a cloud-based command center.

24. The network of claim 21, wherein the Al command center is remote from the edge node device and communicates with the edge node device via cloud-based computing.

25. The edge node device of claim 20, wherein:the edge node device is configured for sending a plurality of control signals to a respective plurality of edge appliances, each of which has a thermostatic or other control input; the controller comprising a primary control relay and a plurality of secondary control relays including the first secondary relay; each of the secondary control relays is respectively arranged in series with the primary control relay; each of the secondary control relays comprising a respective second Al command input, and each of the second Al command inputs being configured to be in communication with the Al command center for receiving a respective second Al command signal; each of the secondary control relays has an open state and a closed state; the controller has a plurality of respective outputs each configured to be in communication with a respective one of the plurality of edge appliances and configured to send a respective control signal from the controller to the respective edge appliance, and the edge node device is configured such that, for each respective edge appliance of the plurality of edge appliances, when the state of the primary control relay is open, a continuously ON command signal is passed through the primary control relay to the respective secondary control relay, and the output of the primary control relay is the Al command signal, when the state of the primary control relay is closed, the output of the primary control relay is a thermostatic command signal generated by the thermostat, when the state of the respective secondary control relay is closed, the output of the primary control relay is sent to the Al command center to be processed by one or more Al control algorithms and form a processed signal that is passed to the respective edge appliance as the control signal, and when the state of the respective secondary control relay is open, an OFF command signal is generated as the control signal regardless of the state of the primary control relay.

26. A network comprising the edge node device of claim 20 and an Al command center, wherein the Al command center is configured to generate the first Al command signal.

27. A system comprising the edge node device of claim 20, an HVACR appliance, a current sensor, and an air duct temperature sensor, wherein the current sensor is configured to send a sensed current signal to the controller, the air duct temperature sensor is configured to send asensed air duct temperature signal to the controller, and the controller is configured to send data pertaining to the sensed current signal and the sensed air duct temperature signal to the Al cloud command center.

28. The system of claim 27, further comprising an Al command center.

29. The system of claim 28, further comprising a cloud-based learning database of curated data, wherein the Al command center is in communication with and configured to retrieve data from the cloud-based learning database of curated data.

30. A non-transitory computer readable storage medium storing instructions which, when executed by a computer, cause the computer to execute a process, the process comprising: receiving conditioning data pertaining to a thermostatic or other control output of the edge device appliance in a conditioned space or other load, the conditioning data comprising one or more of control inputs, state variable inputs, and sensor inputs; processing the conditioning data using artificial intelligence (Al) software to create an optimized thermostatic or other control signal for the edge device appliance in the conditioned space or other load; controlling the edge device appliance in the conditioned space or other load based on the optimized thermostatic or other control signal; receiving an energy consumption and / or demand modification command, the energy consumption and / or demand modification command including or activating a retrieval of modification data, the modification data comprising a demand reduction command value, a time-of-day energy rate, weather information, or a combination thereof; receiving power consumption data from sensors to determine real-time energy consumption and / or demand data of the edge device appliance in the conditioned space; generating an energy consumption and or demand modification signal using the Al software, the energy consumption and / or demand modification signal being based on the conditioning data, the modification data, and the real-time energy consumption and demand data; and controlling the edge device appliance in the conditioned space or other load based on the energy consumption and / or demand modification signal, wherein the energy consumption and / or demand modification signal comprises a modification of timing of ON / OFF commands for the appliances to achieve energy consumption and / or demand reduction.

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